From 79ff52c24c40d7df5ce764244c8dccc2a3a3544c Mon Sep 17 00:00:00 2001
From: art <artlinkov@gmail.com>
Date: Tue, 5 Jun 2018 21:46:29 +0300
Subject: [PATCH 1/8] Added evolutionary optimizer (WIP)

---
 spec/linear_data/.~lock.data.csv# |   1 -
 spec/network_spec.cr              | 497 ++++++++++++++++--------------
 src/shainet/basic/es.cr           | 105 +++++++
 src/shainet/basic/network.cr      | 148 ++++++---
 4 files changed, 487 insertions(+), 264 deletions(-)
 delete mode 100644 spec/linear_data/.~lock.data.csv#
 create mode 100644 src/shainet/basic/es.cr

diff --git a/spec/linear_data/.~lock.data.csv# b/spec/linear_data/.~lock.data.csv#
deleted file mode 100644
index f87136c..0000000
--- a/spec/linear_data/.~lock.data.csv#
+++ /dev/null
@@ -1 +0,0 @@
-,art,art-ThinkPad-P51s-W10DG,21.05.2018 13:30,file:///home/art/.config/libreoffice/4;
\ No newline at end of file
diff --git a/spec/network_spec.cr b/spec/network_spec.cr
index 7b07150..2a6a1ab 100644
--- a/spec/network_spec.cr
+++ b/spec/network_spec.cr
@@ -5,263 +5,263 @@ require "csv"
 system("cd #{__DIR__}/test_data && tar xvf tests.tar.xz")
 
 describe SHAInet::Network do
-  it "Test on a linear regression model" do
-    # data structures to hold the input and results
-    inputs = Array(Array(Float64)).new
-    outputs = Array(Array(Float64)).new
+  # it "Test on a linear regression model" do
+  #   # data structures to hold the input and results
+  #   inputs = Array(Array(Float64)).new
+  #   outputs = Array(Array(Float64)).new
 
-    # read the file
-    raw = File.read("./spec/linear_data/data.csv")
-    csv = CSV.new(raw, headers: true)
+  #   # read the file
+  #   raw = File.read("./spec/linear_data/data.csv")
+  #   csv = CSV.new(raw, headers: true)
 
-    # load the data structures
-    while (csv.next)
-      inputs << [csv.row["Height"].to_f64]
-      outputs << [csv.row["Weight"].to_f64]
-    end
+  #   # load the data structures
+  #   while (csv.next)
+  #     inputs << [csv.row["Height"].to_f64]
+  #     outputs << [csv.row["Weight"].to_f64]
+  #   end
 
-    # normalize the data
-    training = SHAInet::TrainingData.new(inputs, outputs)
+  #   # normalize the data
+  #   training = SHAInet::TrainingData.new(inputs, outputs)
 
-    # create a network
-    model = SHAInet::Network.new
-    model.add_layer(:input, 1, :memory, SHAInet.none)
-    # model.add_layer(:hidden, 1, :memory, SHAInet.none)
-    model.add_layer(:output, 1, :memory, SHAInet.none)
-    model.fully_connect
+  #   # create a network
+  #   model = SHAInet::Network.new
+  #   model.add_layer(:input, 1, :memory, SHAInet.none)
+  #   # model.add_layer(:hidden, 1, :memory, SHAInet.none)
+  #   model.add_layer(:output, 1, :memory, SHAInet.none)
+  #   model.fully_connect
 
-    # Update learing rate (default is 0.005)
-    model.learning_rate = 0.01
+  #   # Update learing rate (default is 0.005)
+  #   model.learning_rate = 0.01
 
-    # train the network using Stochastic Gradient Descent with momentum
-    model.train(training.raw_data, :sgdm, :mse, 5000, 0.0, 100)
+  #   # train the network using Stochastic Gradient Descent with momentum
+  #   model.train(training.raw_data, :adam, :mse, 5000, 0.0, 1)
 
-    # model.show
+  #   # model.show
 
-    # Test model
-    output = model.run([1.47]).first
-    error = ((output - 51.008)/51.008).abs
-    (error < 0.05).should eq(true) # require less than 5% error
+  #   # Test model
+  #   output = model.run([1.47]).first
+  #   error = ((output - 51.008)/51.008).abs
+  #   (error < 0.05).should eq(true) # require less than 5% error
 
-    output = model.run([1.83]).first
-    error = ((output - 73.066)/73.066).abs
-    (error < 0.05).should eq(true) # require less than 5% error
-  end
+  #   output = model.run([1.83]).first
+  #   error = ((output - 73.066)/73.066).abs
+  #   (error < 0.05).should eq(true) # require less than 5% error
+  # end
 
-  it "Initialize" do
-    nn = SHAInet::Network.new
-    nn.should be_a(SHAInet::Network)
-  end
+  # it "Initialize" do
+  #   nn = SHAInet::Network.new
+  #   nn.should be_a(SHAInet::Network)
+  # end
 
-  it "saves_to_file" do
-    nn = SHAInet::Network.new
-    nn.add_layer(:input, 2, :memory, SHAInet.sigmoid)
-    nn.add_layer(:output, 2, :memory, SHAInet.sigmoid)
-    nn.add_layer(:hidden, 2, :memory, SHAInet.sigmoid)
-    nn.fully_connect
-    nn.save_to_file("./my_net.nn")
-    File.exists?("./my_net.nn").should eq(true)
-  end
+  # it "saves_to_file" do
+  #   nn = SHAInet::Network.new
+  #   nn.add_layer(:input, 2, :memory, SHAInet.sigmoid)
+  #   nn.add_layer(:output, 2, :memory, SHAInet.sigmoid)
+  #   nn.add_layer(:hidden, 2, :memory, SHAInet.sigmoid)
+  #   nn.fully_connect
+  #   nn.save_to_file("./my_net.nn")
+  #   File.exists?("./my_net.nn").should eq(true)
+  # end
 
-  it "loads_from_file" do
-    nn = SHAInet::Network.new
-    nn.load_from_file("./my_net.nn")
-    (nn.all_neurons.size > 0).should eq(true)
-  end
+  # it "loads_from_file" do
+  #   nn = SHAInet::Network.new
+  #   nn.load_from_file("./my_net.nn")
+  #   (nn.all_neurons.size > 0).should eq(true)
+  # end
 
-  it "Figure out XOR with SGD + M" do
-    puts "---"
-    puts "Figure out XOR SGD + momentum (train, mse, sigmoid)"
-    training_data = [
-      [[0, 0], [0]],
-      [[1, 0], [1]],
-      [[0, 1], [1]],
-      [[1, 1], [0]],
-    ]
-
-    xor = SHAInet::Network.new
-
-    xor.add_layer(:input, 2, "memory", SHAInet.sigmoid)
-    1.times { |x| xor.add_layer(:hidden, 3, "memory", SHAInet.sigmoid) }
-    xor.add_layer(:output, 1, "memory", SHAInet.sigmoid)
-    xor.fully_connect
-
-    xor.learning_rate = 0.7
-    xor.momentum = 0.3
-
-    xor.train(
-      data: training_data,
-      training_type: :sgdm,
-      cost_function: :mse,
-      epochs: 5000,
-      error_threshold: 0.000001,
-      log_each: 1000)
-
-    (xor.run([0, 0]).first < 0.1).should eq(true)
-    (xor.run([1, 0]).first > 0.9).should eq(true)
-    (xor.run([0, 1]).first > 0.9).should eq(true)
-    (xor.run([1, 1]).first < 0.1).should eq(true)
-  end
+  # it "Figure out XOR with SGD + M" do
+  #   puts "---"
+  #   puts "Figure out XOR SGD + momentum (train, mse, sigmoid)"
+  #   training_data = [
+  #     [[0, 0], [0]],
+  #     [[1, 0], [1]],
+  #     [[0, 1], [1]],
+  #     [[1, 1], [0]],
+  #   ]
+
+  #   xor = SHAInet::Network.new
+
+  #   xor.add_layer(:input, 2, "memory", SHAInet.sigmoid)
+  #   1.times { |x| xor.add_layer(:hidden, 3, "memory", SHAInet.sigmoid) }
+  #   xor.add_layer(:output, 1, "memory", SHAInet.sigmoid)
+  #   xor.fully_connect
+
+  #   xor.learning_rate = 0.7
+  #   xor.momentum = 0.3
+
+  #   xor.train(
+  #     data: training_data,
+  #     training_type: :sgdm,
+  #     cost_function: :mse,
+  #     epochs: 5000,
+  #     error_threshold: 0.000001,
+  #     log_each: 1000)
 
-  it "Supports both Symbols or Strings as input params" do
-    puts "---"
-    puts "Supports both Symbols or Strings as input params (sgdm, train, mse, sigmoid)"
-    training_data = [
-      [[0, 0], [0]],
-      [[1, 0], [1]],
-      [[0, 1], [1]],
-      [[1, 1], [0]],
-    ]
-
-    xor = SHAInet::Network.new
-    xor.add_layer("input", 2, "memory", SHAInet.sigmoid)
-    1.times { |x| xor.add_layer("hidden", 3, "memory", SHAInet.sigmoid) }
-    xor.add_layer("output", 1, "memory", SHAInet.sigmoid)
-    xor.fully_connect
-
-    xor.learning_rate = 0.7
-    xor.momentum = 0.3
-
-    xor.train(
-      data: training_data,
-      training_type: "sgdm",
-      cost_function: "mse",
-      epochs: 5000,
-      error_threshold: 0.000001,
-      log_each: 1000)
-
-    (xor.run([0, 0]).first < 0.1).should eq(true)
-    (xor.run([1, 0]).first > 0.9).should eq(true)
-    (xor.run([0, 1]).first > 0.9).should eq(true)
-    (xor.run([1, 1]).first < 0.1).should eq(true)
-  end
+  #   (xor.run([0, 0]).first < 0.1).should eq(true)
+  #   (xor.run([1, 0]).first > 0.9).should eq(true)
+  #   (xor.run([0, 1]).first > 0.9).should eq(true)
+  #   (xor.run([1, 1]).first < 0.1).should eq(true)
+  # end
 
-  it "works on iris dataset with batch train with Rprop (batch)" do
-    puts "---"
-    puts "works on iris dataset with Rprop (batch_train, mse, sigmoid)"
-    label = {
-      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-    }
-    iris = SHAInet::Network.new
-    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-    iris.fully_connect
+  # it "Supports both Symbols or Strings as input params" do
+  #   puts "---"
+  #   puts "Supports both Symbols or Strings as input params (sgdm, train, mse, sigmoid)"
+  #   training_data = [
+  #     [[0, 0], [0]],
+  #     [[1, 0], [1]],
+  #     [[0, 1], [1]],
+  #     [[1, 1], [0]],
+  #   ]
+
+  #   xor = SHAInet::Network.new
+  #   xor.add_layer("input", 2, "memory", SHAInet.sigmoid)
+  #   1.times { |x| xor.add_layer("hidden", 3, "memory", SHAInet.sigmoid) }
+  #   xor.add_layer("output", 1, "memory", SHAInet.sigmoid)
+  #   xor.fully_connect
+
+  #   xor.learning_rate = 0.7
+  #   xor.momentum = 0.3
+
+  #   xor.train(
+  #     data: training_data,
+  #     training_type: "sgdm",
+  #     cost_function: "mse",
+  #     epochs: 5000,
+  #     error_threshold: 0.000001,
+  #     log_each: 1000)
 
-    iris.learning_rate = 0.7
-    iris.momentum = 0.3
+  #   (xor.run([0, 0]).first < 0.1).should eq(true)
+  #   (xor.run([1, 0]).first > 0.9).should eq(true)
+  #   (xor.run([0, 1]).first > 0.9).should eq(true)
+  #   (xor.run([1, 1]).first < 0.1).should eq(true)
+  # end
 
-    outputs = Array(Array(Float64)).new
-    inputs = Array(Array(Float64)).new
-    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-      row_arr = Array(Float64).new
-      row[0..-2].each do |num|
-        row_arr << num.to_f64
-      end
-      inputs << row_arr
-      outputs << label[row[-1]]
-    end
-    normalized = SHAInet::TrainingData.new(inputs, outputs)
-    normalized.normalize_min_max
+  # it "works on iris dataset with batch train with Rprop (batch)" do
+  #   puts "---"
+  #   puts "works on iris dataset with Rprop (batch_train, mse, sigmoid)"
+  #   label = {
+  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+  #   }
+  #   iris = SHAInet::Network.new
+  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+  #   iris.fully_connect
 
-    iris.train_batch(
-      data: normalized.data.shuffle,
-      training_type: :rprop,
-      cost_function: :mse,
-      epochs: 5000,
-      error_threshold: 0.000001,
-      log_each: 1000)
+  #   iris.learning_rate = 0.7
+  #   iris.momentum = 0.3
 
-    result = iris.run(normalized.normalized_inputs.first)
-    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
-  end
+  #   outputs = Array(Array(Float64)).new
+  #   inputs = Array(Array(Float64)).new
+  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+  #     row_arr = Array(Float64).new
+  #     row[0..-2].each do |num|
+  #       row_arr << num.to_f64
+  #     end
+  #     inputs << row_arr
+  #     outputs << label[row[-1]]
+  #   end
+  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
+  #   normalized.normalize_min_max
 
-  it "works on iris dataset with batch train with Adam (batch)" do
-    puts "---"
-    puts "works on iris dataset with Adam (batch_train, mse, sigmoid)"
-    label = {
-      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-    }
-    iris = SHAInet::Network.new
-    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-    iris.fully_connect
+  #   iris.train_batch(
+  #     data: normalized.data.shuffle,
+  #     training_type: :rprop,
+  #     cost_function: :mse,
+  #     epochs: 5000,
+  #     error_threshold: 0.000001,
+  #     log_each: 1000)
 
-    iris.learning_rate = 0.7
-    iris.momentum = 0.3
+  #   result = iris.run(normalized.normalized_inputs.first)
+  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
+  # end
 
-    outputs = Array(Array(Float64)).new
-    inputs = Array(Array(Float64)).new
-    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-      row_arr = Array(Float64).new
-      row[0..-2].each do |num|
-        row_arr << num.to_f64
-      end
-      inputs << row_arr
-      outputs << label[row[-1]]
-    end
-    normalized = SHAInet::TrainingData.new(inputs, outputs)
-    normalized.normalize_min_max
+  # it "works on iris dataset with batch train with Adam (batch)" do
+  #   puts "---"
+  #   puts "works on iris dataset with Adam (batch_train, mse, sigmoid)"
+  #   label = {
+  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+  #   }
+  #   iris = SHAInet::Network.new
+  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+  #   iris.fully_connect
 
-    iris.train_batch(
-      data: normalized.data.shuffle,
-      training_type: :adam,
-      cost_function: :mse,
-      epochs: 20000,
-      error_threshold: 0.000001,
-      log_each: 1000)
+  #   iris.learning_rate = 0.7
+  #   iris.momentum = 0.3
 
-    result = iris.run(normalized.normalized_inputs.first)
-    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
-  end
+  #   outputs = Array(Array(Float64)).new
+  #   inputs = Array(Array(Float64)).new
+  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+  #     row_arr = Array(Float64).new
+  #     row[0..-2].each do |num|
+  #       row_arr << num.to_f64
+  #     end
+  #     inputs << row_arr
+  #     outputs << label[row[-1]]
+  #   end
+  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
+  #   normalized.normalize_min_max
 
-  it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
-    puts "---"
-    puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
-    label = {
-      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-    }
-    iris = SHAInet::Network.new
-    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-    iris.fully_connect
+  #   iris.train_batch(
+  #     data: normalized.data.shuffle,
+  #     training_type: :adam,
+  #     cost_function: :mse,
+  #     epochs: 20000,
+  #     error_threshold: 0.000001,
+  #     log_each: 1000)
 
-    iris.learning_rate = 0.7
-    iris.momentum = 0.3
+  #   result = iris.run(normalized.normalized_inputs.first)
+  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
+  # end
 
-    outputs = Array(Array(Float64)).new
-    inputs = Array(Array(Float64)).new
-    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-      row_arr = Array(Float64).new
-      row[0..-2].each do |num|
-        row_arr << num.to_f64
-      end
-      inputs << row_arr
-      outputs << label[row[-1]]
-    end
-    normalized = SHAInet::TrainingData.new(inputs, outputs)
-    normalized.normalize_min_max
+  # it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
+  #   puts "---"
+  #   puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
+  #   label = {
+  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+  #   }
+  #   iris = SHAInet::Network.new
+  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+  #   iris.fully_connect
 
-    iris.train_batch(
-      data: normalized.data.shuffle,
-      training_type: :adam,
-      cost_function: :mse,
-      epochs: 5000,
-      error_threshold: 0.000001,
-      mini_batch_size: 50,
-      log_each: 1000)
+  #   iris.learning_rate = 0.7
+  #   iris.momentum = 0.3
 
-    result = iris.run(normalized.normalized_inputs.first)
-    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
-  end
+  #   outputs = Array(Array(Float64)).new
+  #   inputs = Array(Array(Float64)).new
+  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+  #     row_arr = Array(Float64).new
+  #     row[0..-2].each do |num|
+  #       row_arr << num.to_f64
+  #     end
+  #     inputs << row_arr
+  #     outputs << label[row[-1]]
+  #   end
+  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
+  #   normalized.normalize_min_max
+
+  #   iris.train_batch(
+  #     data: normalized.data.shuffle,
+  #     training_type: :adam,
+  #     cost_function: :mse,
+  #     epochs: 5000,
+  #     error_threshold: 0.000001,
+  #     mini_batch_size: 50,
+  #     log_each: 1000)
+
+  #   result = iris.run(normalized.normalized_inputs.first)
+  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
+  # end
 
   # it "trains , saves, loads, runs" do
   #   puts "---"
@@ -309,6 +309,46 @@ describe SHAInet::Network do
   #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   # end
 
+  it "works on iris dataset using evolutionary strategies as optimizer" do
+    puts "---"
+    puts "works on iris dataset using evolutionary strategies as optimizer"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
+    normalized = SHAInet::TrainingData.new(inputs, outputs)
+    normalized.normalize_min_max
+
+    iris.train_es(
+      data: normalized.data.shuffle,
+      pool_size: 1000,
+      cost_function: :mse,
+      epochs: 10,
+      mini_batch_size: 10,
+      error_threshold: 0.0,
+      log_each: 1)
+
+    result = iris.run(normalized.normalized_inputs.first)
+    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
+  end
+
   # it "works on the mnist dataset using adam and batch" do
   #   mnist = SHAInet::Network.new
   #   mnist.add_layer(:input, 784, "memory", SHAInet.sigmoid)
@@ -378,6 +418,7 @@ describe SHAInet::Network do
   #   end
   #   puts "We managed #{results.sum} out of #{results.size} total"
   # end
+
 end
 # Remove train data
 system("cd #{__DIR__}/test_data && rm *.csv")
diff --git a/src/shainet/basic/es.cr b/src/shainet/basic/es.cr
new file mode 100644
index 0000000..e552800
--- /dev/null
+++ b/src/shainet/basic/es.cr
@@ -0,0 +1,105 @@
+module SHAInet
+  class Pool
+    property mvp : Organism
+
+    @organisms : Array(Organism)
+
+    def initialize(@network : Network, @pool_size : Int32)
+      # Store previous data to avoid moving towards worse network states
+      @original_biases = Array(Float64).new
+      @original_weights = Array(Float64).new
+      @organisms = Array(Organism).new(@pool_size) { Organism.new(pool: self) }
+      @mvp = @organisms.sample
+    end
+
+    def save_nn_params
+      @network.all_neurons.each { |neuron| @original_biases << neuron.bias }
+      @network.all_synapses.each { |synapse| @original_weights << synapse.weight }
+    end
+
+    def restore_nn_params
+      @network.all_neurons.each_with_index { |neuron, i| neuron.bias = @original_biases[i] }
+      @network.all_synapses.each_with_index { |synapse, i| synapse.weight = @original_weights[i] }
+    end
+
+    def reset
+      @organisms = Array(Organism).new(@pool_size) { Organism.new(pool: self) }
+    end
+
+    def natural_select(error_threshold : Float64)
+      @organisms.each do |organism|
+        if organism.mse > error_threshold
+          organism.reset
+        end
+      end
+    end
+  end
+
+  class Organism
+    property mse : Float64
+
+    @pool : Pool
+    @learning_rate : Float64
+    @mutation_chance : Float64
+
+    def initialize(@pool : Pool)
+      @learning_rate = rand(0.0..1.0)
+      @mutation_chance = rand(0.0..1.0)
+      @mse = 100000.0
+      @biases = @pool.original_biases.clone
+      @weights = @pool.original_weights.clone
+    end
+
+    def reset
+      @learning_rate = rand(0.0..1.0)
+      @mutation_chance = rand(0.0..1.0)
+      @mse = 100000.0
+      @biases = @pool.original_biases.clone
+      @weights = @pool.original_weights.clone
+    end
+
+    def get_new_params
+      # Update biases
+      @pool.network.all_neurons.each_with_index do |neuron, i|
+        # Only change value if mutation is triggered
+        # This alows for some of the values to remain between epochs
+        if rand(0.0..1.0) < @mutation_chance
+          # Update networks biases using the organisms specific parameters
+          threshold = (@learning_rate*@pool.original_biases[i]).abs
+
+          change = rand(-threshold..threshold)
+          new_value = @pool.original_biases[i] + change
+          neuron.bias = new_value
+          @biases[i] = new_value
+        end
+      end
+
+      # Update weights
+      @pool.network.all_synapses.each_with_index do |synapse, i|
+        # Only change value if mutation is triggered
+        # This alows for some of the values to remain between epochs
+        if rand(0.0..1.0) < @mutation_chance
+          # Update networks biases using the organisms specific parameters
+          threshold = (@learning_rate*@pool.original_weights[i]).abs
+
+          change = rand(-threshold..threshold)
+          new_value = @pool.original_weights + change
+          synapse.weight = new_value
+          @weights[i] = new_value
+        end
+      end
+    end
+
+    def pull_params
+      # Update biases
+      @pool.network.all_neurons.each_with_index do |neuron, i|
+        neuron.bias = @biases[i].clone
+      end
+
+      # Update weights
+      @pool.network.all_synapses.each_with_index do |synapse, i|
+        synapse.weight = @weights[i].clone
+      end
+    end
+  end
+end
diff --git a/src/shainet/basic/network.cr b/src/shainet/basic/network.cr
index 2dd094f..a70465d 100644
--- a/src/shainet/basic/network.cr
+++ b/src/shainet/basic/network.cr
@@ -14,14 +14,14 @@ module SHAInet
 
     # General network parameters
     getter :input_layers, :output_layers, :hidden_layers, :all_neurons, :all_synapses
-    getter error_signal : Array(Float64), total_error : Float64, :mean_error, w_gradient : Array(Float64), b_gradient : Array(Float64)
+    getter error_signal : Array(Float64), total_error : Float64, :mse, w_gradient : Array(Float64), b_gradient : Array(Float64)
 
     # Parameters for SGD + Momentum
     property learning_rate : Float64, momentum : Float64
 
     # Parameters for Rprop
     property etah_plus : Float64, etah_minus : Float64, delta_max : Float64, delta_min : Float64
-    getter prev_mean_error : Float64
+    getter prev_mse : Float64
 
     # Parameters for Adam
     property alpha : Float64
@@ -36,18 +36,18 @@ module SHAInet
       @all_synapses = Array(Synapse).new # Array of all current synapses in the network
       @error_signal = Array(Float64).new # Array of errors for each neuron in the output layers, based on specific input
       @total_error = Float64.new(1)      # Sum of errors from output layer, based on a specific input
-      @mean_error = Float64.new(1)       # MSE of netwrok, based on all errors of output layer for a specific input or batch
+      @mse = Float64.new(1)              # MSE of netwrok, based on all errors of output layer for a specific input or batch
       @w_gradient = Array(Float64).new   # Needed for batch train
       @b_gradient = Array(Float64).new   # Needed for batch train
 
       @learning_rate = 0.005 # Standard parameter for GD
       @momentum = 0.05       # Improved GD
 
-      @etah_plus = 1.2                           # For iRprop+ , how to increase step size
-      @etah_minus = 0.5                          # For iRprop+ , how to decrease step size
-      @delta_max = 50.0                          # For iRprop+ , max step size
-      @delta_min = 0.1                           # For iRprop+ , min step size
-      @prev_mean_error = rand(0.001..1.0).to_f64 # For iRprop+ , needed for backtracking
+      @etah_plus = 1.2                    # For iRprop+ , how to increase step size
+      @etah_minus = 0.5                   # For iRprop+ , how to decrease step size
+      @delta_max = 50.0                   # For iRprop+ , max step size
+      @delta_min = 0.1                    # For iRprop+ , min step size
+      @prev_mse = rand(0.001..1.0).to_f64 # For iRprop+ , needed for backtracking
 
       @alpha = 0.001        # For Adam , step size (recomeneded: only change this hyper parameter when fine-tuning)
       @beta1 = 0.9          # For Adam , exponential decay rate (not recommended to change value)
@@ -256,6 +256,18 @@ module SHAInet
       raise NeuralNetRunError.new("Error in evaluate: #{e}")
     end
 
+    # Calculate MSE from the error signal of the output layer
+    def update_mse
+      if @error_signal.size == 1
+        error_avg = 0.0
+      else
+        error_avg = @total_error/@output_layers.last.neurons.size
+      end
+      sqrd_dists = 0.0
+      @error_signal.each { |e| sqrd_dists += (e - error_avg)**2 }
+      @mse = sqrd_dists/@output_layers.last.neurons.size
+    end
+
     def verify_data(data : Array(Array(Array(GenNum))))
       message = nil
       if data.sample.size != 2
@@ -281,7 +293,7 @@ module SHAInet
     end
 
     def log_summary(e)
-      @logger.info("Epoch: #{e}, Total error: #{@total_error}, MSE: #{@mean_error}")
+      @logger.info("Epoch: #{e}, Total error: #{@total_error}, MSE: #{@mse}")
     end
 
     # Online train, updates weights/biases after each data point (stochastic gradient descent)
@@ -298,12 +310,12 @@ module SHAInet
         if e % log_each == 0
           log_summary(e)
         end
-        if e >= epochs || (error_threshold >= @mean_error) && (e > 0)
+        if e >= epochs || (error_threshold >= @mse) && (e > 0)
           log_summary(e)
           break
         end
 
-        # Change String/Symbol into the corrent proc
+        # Change String/Symbol into the corrent proc of the cost function
         if cost_function.is_a?(Symbol) || cost_function.is_a?(String)
           raise NeuralNetRunError.new("Must define correct cost function type (:mse, :c_ent, :exp, :hel_d, :kld, :gkld, :ita_sai_d).") if COST_FUNCTIONS.any? { |x| x == cost_function.to_s } == false
           proc = get_cost_proc(cost_function.to_s)
@@ -313,35 +325,23 @@ module SHAInet
         # Go over each data point and update the weights/biases based on the specific example
         data.each do |data_point|
           # Update error signal, error gradient and total error at the output layer based on current input
-          # puts "Data-point: #{data_point} [[input], [expected]]"
           evaluate(data_point[0], data_point[1], cost_function)
+          update_mse
 
-          # Propogate the errors backwards through the hidden layers
+          # Propogate the errors backwards
           @hidden_layers.reverse_each do |l|
             l.neurons.each { |neuron| neuron.hidden_error_prop } # Update neuron error based on errors*weights of neurons from the next layer
           end
 
-          # Propogate the errors backwards through the input layers
           @input_layers.reverse_each do |l|
             l.neurons.each { |neuron| neuron.hidden_error_prop } # Update neuron error based on errors*weights of neurons from the next layer
           end
 
-          # Calculate MSE
-          if @error_signal.size == 1
-            error_avg = 0.0
-          else
-            error_avg = @total_error/@output_layers.last.neurons.size
-          end
-          sqrd_dists = [] of Float64
-          @error_signal.each { |e| sqrd_dists << (e - error_avg)**2 }
-          sqr_sum = sqrd_dists.reduce { |acc, i| acc + i }
-          @mean_error = sqr_sum/@output_layers.last.neurons.size
-
           # Update all wieghts & biases
           update_weights(training_type, batch = false)
           update_biases(training_type, batch = false)
 
-          @prev_mean_error = @mean_error
+          @prev_mse = @mse.clone
         end
       end
     rescue e : Exception
@@ -364,7 +364,7 @@ module SHAInet
       batch_size = mini_batch_size ? mini_batch_size : raw_data.size
       @time_step = 0
 
-      # Change String/Symbol into the corrent proc
+      # Change String/Symbol into the corrent proc of the cost function
       if cost_function.is_a?(Symbol) || cost_function.is_a?(String)
         raise NeuralNetRunError.new("Must define correct cost function type (:mse, :c_ent, :exp, :hel_d, :kld, :gkld, :ita_sai_d).") if COST_FUNCTIONS.any? { |x| x == cost_function.to_s } == false
         proc = get_cost_proc(cost_function.to_s)
@@ -380,7 +380,7 @@ module SHAInet
             log_summary(e)
             # @all_neurons.each { |s| puts s.gradient }
           end
-          if e >= epochs || (error_threshold >= @mean_error) && (e > 1)
+          if e >= epochs || (error_threshold >= @mse) && (e > 1)
             log_summary(e)
             break
           end
@@ -418,8 +418,8 @@ module SHAInet
             sqrd_dists = [] of Float64
             @error_signal.each { |e| sqrd_dists << (e - error_avg)**2 }
 
-            @mean_error = (sqrd_dists.reduce { |acc, i| acc + i })/@output_layers.last.neurons.size
-            batch_mean << @mean_error
+            @mse = (sqrd_dists.reduce { |acc, i| acc + i })/@output_layers.last.neurons.size
+            batch_mean << @mse
           end
 
           # Total error per batch
@@ -427,14 +427,14 @@ module SHAInet
 
           # Calculate MSE per batch
           batch_mean = (batch_mean.reduce { |acc, i| acc + i })/data_slice.size
-          @mean_error = batch_mean
+          @mse = batch_mean
 
           # Update all wieghts & biases for the batch
           @time_step += 1 unless mini_batch_size # Based on how many epochs have passed in current training run, needed for Adam
           update_weights(training_type, batch = true)
           update_biases(training_type, batch = true)
 
-          @prev_mean_error = @mean_error
+          @prev_mse = @mse.clone
         end
       end
     end
@@ -469,7 +469,7 @@ module SHAInet
           elsif synapse.prev_gradient*synapse.gradient < 0.0
             delta = [@etah_minus*synapse.prev_delta, @delta_min].max
 
-            synapse.weight -= synapse.prev_delta_w if @mean_error >= @prev_mean_error
+            synapse.weight -= synapse.prev_delta_w if @mse >= @prev_mse
 
             synapse.prev_gradient = 0.0
             synapse.prev_delta = delta
@@ -522,7 +522,7 @@ module SHAInet
           elsif neuron.prev_gradient*neuron.gradient < 0.0
             delta = [@etah_minus*neuron.prev_delta, @delta_min].max
 
-            neuron.bias -= neuron.prev_delta_b if @mean_error >= @prev_mean_error
+            neuron.bias -= neuron.prev_delta_b if @mse >= @prev_mse
 
             neuron.prev_gradient = 0.0
             neuron.prev_delta = delta
@@ -548,13 +548,91 @@ module SHAInet
       end
     end
 
+    # Use evolutionary strategies for network optimization instread of gradient based approach
+    def train_es(data : Array(Array(Array(GenNum))) | SHAInet::TrainingData, # Input structure: data = [[Input = [] of Float64],[Expected result = [] of Float64]]
+                 pool_size : Int32,                                          # Pool size for the organisms
+                 cost_function : Symbol | String | CostFunction = :mse,      # Proc returns the function value and it's derivative
+                 epochs : Int32 = 1,                                         # a criteria of when to stop the training
+                 mini_batch_size : Int32 = 1,                                # Size of batch
+                 error_threshold : Float64 = 0.0,                            # a criteria of when to stop the training
+                 log_each : Int32 = 1)                                       # determines what is the step for error printout
+      # This methods accepts data as either a SHAInet::TrainingData object, or as an Array(Array(Array(GenNum)).
+      # In the case of SHAInet::TrainingData, we convert it to an Array(Array(Array(GenNum)) by calling #data on it.
+      raw_data = data.is_a?(SHAInet::TrainingData) ? data.data : data
+      @logger.info("Training started")
+      batch_size = mini_batch_size ? mini_batch_size : raw_data.size
+
+      # Change String/Symbol into the corrent proc of the cost function
+      if cost_function.is_a?(Symbol) || cost_function.is_a?(String)
+        raise NeuralNetRunError.new("Must define correct cost function type (:mse, :c_ent, :exp, :hel_d, :kld, :gkld, :ita_sai_d).") if COST_FUNCTIONS.any? { |x| x == cost_function.to_s } == false
+        proc = get_cost_proc(cost_function.to_s)
+        cost_function = proc
+      end
+
+      # Create a pool for the training
+      pool = Pool.new(network: self, pool_size: pool_size)
+
+      loop do |e|
+        if e % log_each == 0
+          log_summary(e)
+          # @all_neurons.each { |s| puts s.gradient }
+        end
+        if e >= epochs || (error_threshold >= @mse) && (e > 1)
+          log_summary(e)
+          break
+        end
+
+        raw_data.each_slice(batch_size, reuse = false) do |data_slice|
+          verify_data(data_slice)
+          pool.save_nn_params
+          # @logger.info("Working on mini-batch size: #{batch_size}") if mini_batch_size
+
+          lowest_mse = 100000.0
+          # Update wieghts & biases for the batch
+          pool.organisms.each do |organism|
+            organism.get_new_params # Get new weights & biases
+
+            # Go over each data points and collect mse
+            # based on each specific example in the batch
+            batch_mean = 0.0
+            data_slice.each do |data_point|
+              evaluate(data_point[0], data_point[1], cost_function) # Update error signal in output layer
+              update_mse
+              batch_mean += @mse
+            end
+            batch_mean /= mini_batch_size # Update MSE of the batch
+
+            organism.mse = batch_mean
+            if batch_mean < pool.mvp.mse
+              pool.mvp = organism
+              lowest_mse = batch_mean
+            end
+          end
+
+          if pool.mvp.mse < @prev_mse
+            # Update pool for the next batch
+            @mse = pool.mvp.mse
+            pool.natural_select(@mse)
+
+            # Get the best parameters from the pool
+            pool.mvp.pull_params
+            @prev_mse = @mse.clone
+          else
+            # Try new pool on next batch
+            pool.reset
+            pool.restore_nn_params
+          end
+        end
+      end
+    end
+
     def randomize_all_weights
       raise NeuralNetRunError.new("Cannot randomize weights without synapses") if @all_synapses.empty?
       @all_synapses.each &.randomize_weight
     end
 
     def randomize_all_biases
-      raise NeuralNetRunError.new("Cannot randomize biases without synapses") if @all_synapses.empty?
+      raise NeuralNetRunError.new("Cannot randomize biases without neurons") if @all_synapses.empty?
       @all_neurons.each &.randomize_bias
     end
 

From 2775ee8292775df7c85273746fd0bfd21c374ab3 Mon Sep 17 00:00:00 2001
From: Bar Hofesh <bar.hofesh@gmail.com>
Date: Tue, 5 Jun 2018 23:26:11 +0300
Subject: [PATCH 2/8] made the code work

---
 spec/network_spec.cr    |  2 +-
 src/shainet/basic/es.cr | 51 ++++++++++++++++++++++++++---------------
 2 files changed, 34 insertions(+), 19 deletions(-)

diff --git a/spec/network_spec.cr b/spec/network_spec.cr
index 2a6a1ab..6e8de79 100644
--- a/spec/network_spec.cr
+++ b/spec/network_spec.cr
@@ -340,7 +340,7 @@ describe SHAInet::Network do
       data: normalized.data.shuffle,
       pool_size: 1000,
       cost_function: :mse,
-      epochs: 10,
+      epochs: 100,
       mini_batch_size: 10,
       error_threshold: 0.0,
       log_each: 1)
diff --git a/src/shainet/basic/es.cr b/src/shainet/basic/es.cr
index e552800..fcb03f7 100644
--- a/src/shainet/basic/es.cr
+++ b/src/shainet/basic/es.cr
@@ -1,15 +1,21 @@
 module SHAInet
   class Pool
-    property mvp : Organism
+    property :mvp
+    getter :organisms
 
     @organisms : Array(Organism)
+    @mvp : Organism
 
     def initialize(@network : Network, @pool_size : Int32)
       # Store previous data to avoid moving towards worse network states
       @original_biases = Array(Float64).new
       @original_weights = Array(Float64).new
-      @organisms = Array(Organism).new(@pool_size) { Organism.new(pool: self) }
-      @mvp = @organisms.sample
+      @organisms = Array(Organism).new
+      save_nn_params
+      @pool_size.times do
+        @organisms << Organism.new(@network, @original_biases, @original_weights)
+      end
+      @mvp = @organisms.sample.as(Organism)
     end
 
     def save_nn_params
@@ -23,7 +29,10 @@ module SHAInet
     end
 
     def reset
-      @organisms = Array(Organism).new(@pool_size) { Organism.new(pool: self) }
+      @organisms = Array(Organism).new
+      @pool_size.times do
+        @organisms << Organism.new(@network, @original_biases, @original_weights)
+      end
     end
 
     def natural_select(error_threshold : Float64)
@@ -38,52 +47,58 @@ module SHAInet
   class Organism
     property mse : Float64
 
-    @pool : Pool
+    @network : Network
     @learning_rate : Float64
     @mutation_chance : Float64
+    @biases : Array(Float64)
+    @weights : Array(Float64)
 
-    def initialize(@pool : Pool)
+    def initialize(@network : Network, original_biases : Array(Float64), original_weights : Array(Float64))
       @learning_rate = rand(0.0..1.0)
       @mutation_chance = rand(0.0..1.0)
       @mse = 100000.0
-      @biases = @pool.original_biases.clone
-      @weights = @pool.original_weights.clone
+
+      @original_biases = original_biases
+      @original_weights = original_weights
+      @biases = original_biases.clone
+      @weights = original_weights.clone
     end
 
     def reset
       @learning_rate = rand(0.0..1.0)
       @mutation_chance = rand(0.0..1.0)
       @mse = 100000.0
-      @biases = @pool.original_biases.clone
-      @weights = @pool.original_weights.clone
+      @biases = @original_biases.clone
+      @weights = @original_weights.clone
     end
 
     def get_new_params
       # Update biases
-      @pool.network.all_neurons.each_with_index do |neuron, i|
+      @network.all_neurons.each_with_index do |neuron, i|
         # Only change value if mutation is triggered
         # This alows for some of the values to remain between epochs
         if rand(0.0..1.0) < @mutation_chance
           # Update networks biases using the organisms specific parameters
-          threshold = (@learning_rate*@pool.original_biases[i]).abs
+          threshold = (@learning_rate*@original_biases[i]).abs
 
           change = rand(-threshold..threshold)
-          new_value = @pool.original_biases[i] + change
+          new_value = @original_biases[i] + change
           neuron.bias = new_value
+
           @biases[i] = new_value
         end
       end
 
       # Update weights
-      @pool.network.all_synapses.each_with_index do |synapse, i|
+      @network.all_synapses.each_with_index do |synapse, i|
         # Only change value if mutation is triggered
         # This alows for some of the values to remain between epochs
         if rand(0.0..1.0) < @mutation_chance
           # Update networks biases using the organisms specific parameters
-          threshold = (@learning_rate*@pool.original_weights[i]).abs
+          threshold = (@learning_rate*@original_weights[i]).abs
 
           change = rand(-threshold..threshold)
-          new_value = @pool.original_weights + change
+          new_value = @original_weights[i] + change
           synapse.weight = new_value
           @weights[i] = new_value
         end
@@ -92,12 +107,12 @@ module SHAInet
 
     def pull_params
       # Update biases
-      @pool.network.all_neurons.each_with_index do |neuron, i|
+      @network.all_neurons.each_with_index do |neuron, i|
         neuron.bias = @biases[i].clone
       end
 
       # Update weights
-      @pool.network.all_synapses.each_with_index do |synapse, i|
+      @network.all_synapses.each_with_index do |synapse, i|
         synapse.weight = @weights[i].clone
       end
     end

From 344f1f7655f12ccea1bca76fc365359c5f7f96f1 Mon Sep 17 00:00:00 2001
From: art <artlinkov@gmail.com>
Date: Tue, 5 Jun 2018 23:37:38 +0300
Subject: [PATCH 3/8] minor bug fixes

---
 src/shainet/basic/es.cr | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/src/shainet/basic/es.cr b/src/shainet/basic/es.cr
index e552800..c4da7db 100644
--- a/src/shainet/basic/es.cr
+++ b/src/shainet/basic/es.cr
@@ -1,10 +1,9 @@
 module SHAInet
   class Pool
-    property mvp : Organism
+    property :mvp # : Organism
+    getter original_biases : Array(Float64), original_weights : Array(Float64), organisms : Array(Organism)
 
-    @organisms : Array(Organism)
-
-    def initialize(@network : Network, @pool_size : Int32)
+    def initialize(@network : Network, @pool_size : Int32 = 1)
       # Store previous data to avoid moving towards worse network states
       @original_biases = Array(Float64).new
       @original_weights = Array(Float64).new
@@ -41,6 +40,8 @@ module SHAInet
     @pool : Pool
     @learning_rate : Float64
     @mutation_chance : Float64
+    @biases : Array(Float64)
+    @weights : Array(Float64)
 
     def initialize(@pool : Pool)
       @learning_rate = rand(0.0..1.0)
@@ -54,8 +55,8 @@ module SHAInet
       @learning_rate = rand(0.0..1.0)
       @mutation_chance = rand(0.0..1.0)
       @mse = 100000.0
-      @biases = @pool.original_biases.clone
-      @weights = @pool.original_weights.clone
+      # @biases = @pool.original_biases.clone
+      # @weights = @pool.original_weights.clone
     end
 
     def get_new_params
@@ -67,8 +68,7 @@ module SHAInet
           # Update networks biases using the organisms specific parameters
           threshold = (@learning_rate*@pool.original_biases[i]).abs
 
-          change = rand(-threshold..threshold)
-          new_value = @pool.original_biases[i] + change
+          new_value = @pool.original_biases[i] + rand(-threshold..threshold)
           neuron.bias = new_value
           @biases[i] = new_value
         end
@@ -82,8 +82,7 @@ module SHAInet
           # Update networks biases using the organisms specific parameters
           threshold = (@learning_rate*@pool.original_weights[i]).abs
 
-          change = rand(-threshold..threshold)
-          new_value = @pool.original_weights + change
+          new_value = @pool.original_weights + rand(-threshold..threshold)
           synapse.weight = new_value
           @weights[i] = new_value
         end

From 62f63b31c754e919ed75ec4f04c0a0970161f2e7 Mon Sep 17 00:00:00 2001
From: art <artlinkov@gmail.com>
Date: Mon, 11 Jun 2018 16:14:23 +0300
Subject: [PATCH 4/8] Cross-entropy cost now working

---
 spec/network_spec.cr           | 156 +++++++++++++++++++++------------
 src/shainet/basic/es.cr        |  24 ++++-
 src/shainet/basic/functions.cr |  39 ++++-----
 src/shainet/basic/network.cr   |  16 +++-
 4 files changed, 150 insertions(+), 85 deletions(-)

diff --git a/spec/network_spec.cr b/spec/network_spec.cr
index dddc432..cfdf274 100644
--- a/spec/network_spec.cr
+++ b/spec/network_spec.cr
@@ -69,50 +69,50 @@ describe SHAInet::Network do
   #   (nn.all_neurons.size > 0).should eq(true)
   # end
 
-  it "Figure out XOR with SGD + M" do
-    puts "---"
-    puts "Figure out XOR SGD + momentum (train, mse, sigmoid)"
-    training_data = [
-      [[0, 0], [0]],
-      [[1, 0], [1]],
-      [[0, 1], [1]],
-      [[1, 1], [0]],
-    ]
-
-    xor = SHAInet::Network.new
-
-    xor.add_layer(:input, 2, "memory", SHAInet.sigmoid)
-    1.times { |x| xor.add_layer(:hidden, 3, "memory", SHAInet.sigmoid) }
-    xor.add_layer(:output, 1, "memory", SHAInet.sigmoid)
-    xor.fully_connect
-
-    # xor.learning_rate = 0.7
-    # xor.momentum = 0.3
-
-    # xor.train(
-    #   data: training_data,
-    #   training_type: :sgdm,
-    #   cost_function: :mse,
-    #   epochs: 5000,
-    #   error_threshold: 0.000001,
-    #   log_each: 1000)
-
-    xor.train_es(
-      data: training_data,
-      pool_size: 100,
-      learning_rate: 0.2,
-      sigma: 0.3,
-      cost_function: :mse,
-      epochs: 1000,
-      mini_batch_size: 1,
-      error_threshold: 0.0,
-      log_each: 10)
+  # it "Figure out XOR with SGD + M" do
+  #   puts "---"
+  #   puts "Figure out XOR SGD + momentum (train, mse, sigmoid)"
+  #   training_data = [
+  #     [[0, 0], [0]],
+  #     [[1, 0], [1]],
+  #     [[0, 1], [1]],
+  #     [[1, 1], [0]],
+  #   ]
 
-    (xor.run([0, 0]).first < 0.1).should eq(true)
-    (xor.run([1, 0]).first > 0.9).should eq(true)
-    (xor.run([0, 1]).first > 0.9).should eq(true)
-    (xor.run([1, 1]).first < 0.1).should eq(true)
-  end
+  #   xor = SHAInet::Network.new
+
+  #   xor.add_layer(:input, 2, "memory", SHAInet.sigmoid)
+  #   1.times { |x| xor.add_layer(:hidden, 3, "memory", SHAInet.sigmoid) }
+  #   xor.add_layer(:output, 1, "memory", SHAInet.sigmoid)
+  #   xor.fully_connect
+
+  #   # xor.learning_rate = 0.7
+  #   # xor.momentum = 0.3
+
+  #   # xor.train(
+  #   #   data: training_data,
+  #   #   training_type: :sgdm,
+  #   #   cost_function: :mse,
+  #   #   epochs: 5000,
+  #   #   error_threshold: 0.000001,
+  #   #   log_each: 1000)
+
+  #   xor.train_es(
+  #     data: training_data,
+  #     pool_size: 100,
+  #     learning_rate: 0.2,
+  #     sigma: 0.3,
+  #     cost_function: :mse,
+  #     epochs: 1000,
+  #     mini_batch_size: 1,
+  #     error_threshold: 0.0,
+  #     log_each: 10)
+
+  #   (xor.run([0, 0]).first < 0.1).should eq(true)
+  #   (xor.run([1, 0]).first > 0.9).should eq(true)
+  #   (xor.run([0, 1]).first > 0.9).should eq(true)
+  #   (xor.run([1, 1]).first < 0.1).should eq(true)
+  # end
 
   # it "Supports both Symbols or Strings as input params" do
   #   puts "---"
@@ -320,9 +320,9 @@ describe SHAInet::Network do
   #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   # end
 
-  it "works on iris dataset using evolutionary strategies as optimizer" do
+  it "Works with cross-entropy" do
     puts "---"
-    puts "works on iris dataset using evolutionary strategies as optimizer"
+    puts "Works with cross-entropy (sgdm, mini-batch_train, cross-entropy, sigmoid + softmax)"
     label = {
       "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
       "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
@@ -334,6 +334,9 @@ describe SHAInet::Network do
     iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
     iris.fully_connect
 
+    iris.learning_rate = 0.7
+    iris.momentum = 0.3
+
     outputs = Array(Array(Float64)).new
     inputs = Array(Array(Float64)).new
     CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
@@ -347,21 +350,64 @@ describe SHAInet::Network do
     normalized = SHAInet::TrainingData.new(inputs, outputs)
     normalized.normalize_min_max
 
-    iris.train_es(
+    iris.train_batch(
       data: normalized.data.shuffle,
-      pool_size: 50,
-      learning_rate: 0.2,
-      sigma: 0.3,
-      cost_function: :mse,
-      epochs: 10,
-      mini_batch_size: 1,
-      error_threshold: 0.0,
-      log_each: 1)
+      training_type: :sgdm,
+      cost_function: :c_ent,
+      epochs: 100,
+      error_threshold: 0.000001,
+      mini_batch_size: 50,
+      log_each: 10)
 
     result = iris.run(normalized.normalized_inputs.first)
-    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
+    expected = [0.0, 0.0, 1.0]
+    puts "result: \t#{result}"
+    puts "expected: \t#{expected}"
+    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   end
 
+  # it "works on iris dataset using evolutionary strategies as optimizer" do
+  #   puts "---"
+  #   puts "works on iris dataset using evolutionary strategies as optimizer"
+  #   label = {
+  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+  #   }
+  #   iris = SHAInet::Network.new
+  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+  #   iris.fully_connect
+
+  #   outputs = Array(Array(Float64)).new
+  #   inputs = Array(Array(Float64)).new
+  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+  #     row_arr = Array(Float64).new
+  #     row[0..-2].each do |num|
+  #       row_arr << num.to_f64
+  #     end
+  #     inputs << row_arr
+  #     outputs << label[row[-1]]
+  #   end
+  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
+  #   normalized.normalize_min_max
+
+  #   iris.train_es(
+  #     data: normalized.data.shuffle,
+  #     pool_size: 50,
+  #     learning_rate: 0.2,
+  #     sigma: 0.3,
+  #     cost_function: :mse,
+  #     epochs: 10,
+  #     mini_batch_size: 1,
+  #     error_threshold: 0.0,
+  #     log_each: 1)
+
+  #   result = iris.run(normalized.normalized_inputs.first)
+  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
+  # end
+
   # it "works on the mnist dataset using adam and batch" do
   #   mnist = SHAInet::Network.new
   #   mnist.add_layer(:input, 784, "memory", SHAInet.sigmoid)
diff --git a/src/shainet/basic/es.cr b/src/shainet/basic/es.cr
index 6f77703..5a87903 100644
--- a/src/shainet/basic/es.cr
+++ b/src/shainet/basic/es.cr
@@ -14,6 +14,7 @@ module SHAInet
                    @learning_rate : Float64,
                    @sigma : Float64)
       #
+      raise "Pool size must be at least 2" if @pool_size < 2
       # Store previous data to avoid moving towards worse network states
       @pool_biases = Array(Float64).new
       @pool_weights = Array(Float64).new
@@ -70,15 +71,21 @@ module SHAInet
     def pull_params
       normalize_rewards
       norm_value = @learning_rate / (@pool_size * @sigma)
+      # puts "norm_value: #{norm_value}"
 
       @organisms.each do |organism|
         organism.biases.each_with_index do |bias, i|
           weighted_value = bias * organism.reward
-          @pool_biases[i] += norm_value * weighted_value
+          @pool_biases[i] += weighted_value # norm_value * weighted_value
+          # puts "i: #{i}"
+          # puts "organism.error_signal: #{organism.error_signal}"
+          # puts "organism.reward: #{organism.reward}"
+          # puts "bias: #{bias}"
+          # puts "weighted_value: #{weighted_value}"
         end
         organism.weights.each_with_index do |weight, i|
           weighted_value = weight * organism.reward
-          @pool_weights[i] += norm_value * weighted_value
+          @pool_weights[i] += weighted_value # norm_value * weighted_value
         end
       end
 
@@ -121,7 +128,7 @@ module SHAInet
   end
 
   class Organism
-    property mse : Float64, reward : Float64
+    property mse : Float64, error_signal : Array(Float64), reward : Float64
     getter biases : Array(Float64), weights : Array(Float64)
 
     @network : Network
@@ -140,6 +147,7 @@ module SHAInet
       # @sigma = rand(0.0..1.0)
       @mse = 0.0
       @reward = 0.0
+      @error_signal = [] of Float64
 
       @original_biases = original_biases.clone
       @original_weights = original_weights.clone
@@ -209,7 +217,15 @@ module SHAInet
     # end
 
     def update_reward
-      @reward = -@mse.clone # ((@network.prev_mse - @mse) / @network.prev_mse)
+      @error_signal = @network.error_signal.clone
+      @reward = 0.0
+      @error_signal.each { |v| @reward -= v }
+      # reward_sum = -@error_signal.reduce(0.0) { |acc, i| acc + i }
+      # @reward = SHAInet._tanh(reward_sum)
+
+      # puts "@reward: #{@reward}"
+      # puts "@error_signal: #{@error_signal}"
+      # @reward = -@mse.clone # ((@network.prev_mse - @mse) / @network.prev_mse)
       # puts "###############"
       # puts "@network.prev_mse: #{@network.prev_mse}"
       # puts "@mse: #{@mse}"
diff --git a/src/shainet/basic/functions.cr b/src/shainet/basic/functions.cr
index f14f757..552acbe 100644
--- a/src/shainet/basic/functions.cr
+++ b/src/shainet/basic/functions.cr
@@ -137,9 +137,12 @@ module SHAInet
     }
   end
 
-  # def self.quadratic_cost_derivative : Proc(GenNum, GenNum, Float64)
-  #   ->(expected : GenNum, actual : GenNum) { _quadratic_cost_derivative(expected.to_f64, actual.to_f64) }
-  # end
+  def self.cross_entropy_cost : CostFunction
+    ->(expected : GenNum, actual : GenNum) {
+      {value:      _cross_entropy_cost(expected.to_f64, actual.to_f64),
+       derivative: _cross_entropy_cost_derivative(expected.to_f64, actual.to_f64)}
+    }
+  end
 
   # # Cost functions  # #
 
@@ -147,32 +150,24 @@ module SHAInet
     return (0.5*(actual - expected)**2).to_f64
   end
 
-  def self.cross_entropy_cost(expected : Float64, actual : Float64) : Float64
-    raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
-    # a = ((-1)*((expected*Math.log((actual), Math::E) + (1.0 - expected)*Math.log((1.0 - actual), Math::E))**2)**0.5).to_f64
-    # if a.to_s.match(/(-NaN|NaN|Infinity)/i)
-    #   return 0.0
-    # else
-    #   return a
-    # end
+  def self._cross_entropy_cost(expected : Float64, actual : Float64) : Float64
+    # raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
+    if expected == 1.0
+      return (-1)*Math.log((actual), Math::E)
+    elsif expected == 0.0
+      return (-1)*Math.log((1.0 - actual), Math::E)
+    else
+      raise MathError.new("Expected value must be 0 or 1 for cross entropy cost.")
+    end
   end
 
   # # Derivatives of cost functions # #
-
   def self._quadratic_cost_derivative(expected : Float64, actual : Float64) : Float64
     return (actual - expected).to_f64
   end
 
-  def self.cross_entropy_cost_derivative(expected : Float64, actual : Float64) : Float64
-    if actual == expected == 0.0 || actual == expected == 1.0
-      a = 0.0
-    elsif actual == 0.0 && expected != 0.0
-      a = -1.0
-    else
-      a = ((actual - expected)/((1.0 - actual)*actual)).to_f64
-    end
-    # puts a
-    return a
+  def self._cross_entropy_cost_derivative(expected : Float64, actual : Float64) : Float64
+    return (actual - expected).to_f64
   end
 
   ##################################################################
diff --git a/src/shainet/basic/network.cr b/src/shainet/basic/network.cr
index 1a44eff..46443f1 100644
--- a/src/shainet/basic/network.cr
+++ b/src/shainet/basic/network.cr
@@ -598,14 +598,21 @@ module SHAInet
 
             # Go over each data points and collect mse
             # based on each specific example in the batch
-            batch_sum = 0.0
+            batch_mse_sum = 0.0
+            batch_err_sig_sum = Array(Float64).new(@output_layers.last.neurons.size) { 0.0 }
+
             data_slice.each do |data_point|
               evaluate(data_point[0], data_point[1], cost_function) # Update error signal in output layer
               update_mse
-              batch_sum += @mse
+              batch_mse_sum += @mse
+              @error_signal.size.times { |i| batch_err_sig_sum[i] += @error_signal[i] }
+
+              puts "data_point: #{data_point}"
+              puts "@error_signal: #{@error_signal}"
             end
 
-            organism.mse = batch_sum # / mini_batch_size # Update MSE of the batch
+            @mse = batch_mse_sum / mini_batch_size # Update MSE of the batch
+            # organism.mse
             organism.update_reward
 
             #   if organism.mse < pool.mvp.mse
@@ -745,7 +752,8 @@ module SHAInet
       when "mse"
         return SHAInet.quadratic_cost
       when "c_ent"
-        raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
+        # raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
+        return SHAInet.cross_entropy_cost
       else
         raise NeuralNetInitalizationError.new("Must choose correct cost function or provide a correct Proc")
       end

From b7ce40220be80339135d70872f3aad3bb0f7bcc6 Mon Sep 17 00:00:00 2001
From: art <artlinkov@gmail.com>
Date: Mon, 11 Jun 2018 16:15:32 +0300
Subject: [PATCH 5/8] Cross-entropy cost now working

---
 spec/network_spec.cr | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spec/network_spec.cr b/spec/network_spec.cr
index cfdf274..fbb3fb6 100644
--- a/spec/network_spec.cr
+++ b/spec/network_spec.cr
@@ -361,8 +361,8 @@ describe SHAInet::Network do
 
     result = iris.run(normalized.normalized_inputs.first)
     expected = [0.0, 0.0, 1.0]
-    puts "result: \t#{result}"
-    puts "expected: \t#{expected}"
+    puts "result: \t#{result.round(4)}"
+    puts "expected: \t#{expected.round(4)}"
     ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   end
 

From 738e71d3d0b62ade4af8a21bda87590007662f6c Mon Sep 17 00:00:00 2001
From: art <artlinkov@gmail.com>
Date: Mon, 11 Jun 2018 16:26:18 +0300
Subject: [PATCH 6/8] ES optimizer now works (mostly, working on fixing NaNs
 issue)

---
 spec/network_spec.cr         | 139 ++++++++++++++++++-----------------
 src/shainet/basic/network.cr |   6 +-
 2 files changed, 74 insertions(+), 71 deletions(-)

diff --git a/spec/network_spec.cr b/spec/network_spec.cr
index fbb3fb6..c1efeca 100644
--- a/spec/network_spec.cr
+++ b/spec/network_spec.cr
@@ -231,9 +231,52 @@ describe SHAInet::Network do
   #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   # end
 
-  # it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
+  it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
+    puts "---"
+    puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    iris.learning_rate = 0.7
+    iris.momentum = 0.3
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
+    normalized = SHAInet::TrainingData.new(inputs, outputs)
+    normalized.normalize_min_max
+
+    iris.train_batch(
+      data: normalized.data.shuffle,
+      training_type: :adam,
+      cost_function: :mse,
+      epochs: 5000,
+      error_threshold: 0.000001,
+      mini_batch_size: 50,
+      log_each: 1000)
+
+    result = iris.run(normalized.normalized_inputs.first)
+    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
+  end
+
+  # it "trains , saves, loads, runs" do
   #   puts "---"
-  #   puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
+  #   puts "train, save, loads and run works (Adam, mini-batch_train, mse, sigmoid)"
   #   label = {
   #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
   #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
@@ -270,13 +313,16 @@ describe SHAInet::Network do
   #     mini_batch_size: 50,
   #     log_each: 1000)
 
-  #   result = iris.run(normalized.normalized_inputs.first)
+  #   iris.save_to_file("./my_net.nn")
+  #   nn = SHAInet::Network.new
+  #   nn.load_from_file("./my_net.nn")
+  #   result = nn.run(normalized.normalized_inputs.first)
   #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   # end
 
-  # it "trains , saves, loads, runs" do
+  # it "Works with cross-entropy" do
   #   puts "---"
-  #   puts "train, save, loads and run works (Adam, mini-batch_train, mse, sigmoid)"
+  #   puts "Works with cross-entropy (sgdm, mini-batch_train, cross-entropy, sigmoid + softmax)"
   #   label = {
   #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
   #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
@@ -306,23 +352,23 @@ describe SHAInet::Network do
 
   #   iris.train_batch(
   #     data: normalized.data.shuffle,
-  #     training_type: :adam,
-  #     cost_function: :mse,
-  #     epochs: 5000,
+  #     training_type: :sgdm,
+  #     cost_function: :c_ent,
+  #     epochs: 100,
   #     error_threshold: 0.000001,
   #     mini_batch_size: 50,
-  #     log_each: 1000)
+  #     log_each: 10)
 
-  #   iris.save_to_file("./my_net.nn")
-  #   nn = SHAInet::Network.new
-  #   nn.load_from_file("./my_net.nn")
-  #   result = nn.run(normalized.normalized_inputs.first)
+  #   result = iris.run(normalized.normalized_inputs.first)
+  #   expected = [0.0, 0.0, 1.0]
+  #   puts "result: \t#{result.map { |x| x.round(5) }}"
+  #   puts "expected: \t#{expected}"
   #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   # end
 
-  it "Works with cross-entropy" do
+  it "works on iris dataset using evolutionary strategies as optimizer" do
     puts "---"
-    puts "Works with cross-entropy (sgdm, mini-batch_train, cross-entropy, sigmoid + softmax)"
+    puts "works on iris dataset using evolutionary strategies as optimizer"
     label = {
       "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
       "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
@@ -334,9 +380,6 @@ describe SHAInet::Network do
     iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
     iris.fully_connect
 
-    iris.learning_rate = 0.7
-    iris.momentum = 0.3
-
     outputs = Array(Array(Float64)).new
     inputs = Array(Array(Float64)).new
     CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
@@ -350,64 +393,24 @@ describe SHAInet::Network do
     normalized = SHAInet::TrainingData.new(inputs, outputs)
     normalized.normalize_min_max
 
-    iris.train_batch(
+    iris.train_es(
       data: normalized.data.shuffle,
-      training_type: :sgdm,
+      pool_size: 50,
+      learning_rate: 0.2,
+      sigma: 0.3,
       cost_function: :c_ent,
       epochs: 100,
-      error_threshold: 0.000001,
-      mini_batch_size: 50,
-      log_each: 10)
+      mini_batch_size: 1,
+      error_threshold: 0.00001,
+      log_each: 1)
 
     result = iris.run(normalized.normalized_inputs.first)
     expected = [0.0, 0.0, 1.0]
-    puts "result: \t#{result.round(4)}"
-    puts "expected: \t#{expected.round(4)}"
-    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
+    puts "result: \t#{result.map { |x| x.round(5) }}"
+    puts "expected: \t#{expected}"
+    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
   end
 
-  # it "works on iris dataset using evolutionary strategies as optimizer" do
-  #   puts "---"
-  #   puts "works on iris dataset using evolutionary strategies as optimizer"
-  #   label = {
-  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-  #   }
-  #   iris = SHAInet::Network.new
-  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-  #   iris.fully_connect
-
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[0..-2].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     outputs << label[row[-1]]
-  #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
-
-  #   iris.train_es(
-  #     data: normalized.data.shuffle,
-  #     pool_size: 50,
-  #     learning_rate: 0.2,
-  #     sigma: 0.3,
-  #     cost_function: :mse,
-  #     epochs: 10,
-  #     mini_batch_size: 1,
-  #     error_threshold: 0.0,
-  #     log_each: 1)
-
-  #   result = iris.run(normalized.normalized_inputs.first)
-  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
-  # end
-
   # it "works on the mnist dataset using adam and batch" do
   #   mnist = SHAInet::Network.new
   #   mnist.add_layer(:input, 784, "memory", SHAInet.sigmoid)
diff --git a/src/shainet/basic/network.cr b/src/shainet/basic/network.cr
index 46443f1..ffbfb79 100644
--- a/src/shainet/basic/network.cr
+++ b/src/shainet/basic/network.cr
@@ -553,7 +553,7 @@ module SHAInet
                  pool_size : Int32,                                          # How many random direction to try each time
                  learning_rate : Float64,                                    # How much of the noise i used for the parameter update
                  sigma : Float64,                                            # Range of noise values
-                 cost_function : Symbol | String | CostFunction = :mse,      # Proc returns the function value and it's derivative
+                 cost_function : Symbol | String | CostFunction = :c_ent,    # Proc returns the function value and it's derivative
                  epochs : Int32 = 1,                                         # a criteria of when to stop the training
                  mini_batch_size : Int32 = 1,                                # Size of batch
                  error_threshold : Float64 = 0.0,                            # a criteria of when to stop the training
@@ -607,8 +607,8 @@ module SHAInet
               batch_mse_sum += @mse
               @error_signal.size.times { |i| batch_err_sig_sum[i] += @error_signal[i] }
 
-              puts "data_point: #{data_point}"
-              puts "@error_signal: #{@error_signal}"
+              # puts "data_point: #{data_point}"
+              # puts "@error_signal: #{@error_signal}"
             end
 
             @mse = batch_mse_sum / mini_batch_size # Update MSE of the batch

From 8ba30f6d7283bebd2f718177b46f4d80dcaef56c Mon Sep 17 00:00:00 2001
From: art <artlinkov@gmail.com>
Date: Thu, 14 Jun 2018 17:13:19 +0300
Subject: [PATCH 7/8] Evolutionary optimizer done

- Optimizer works
- Added cross-entropy cost
- Added random sampling from normal distribution
- Minor changes to Data class
- Improved specs
---
 README.md                                |    13 +-
 csv_file.csv                             | 10000 +++++++++++++++++++++
 spec/cnn_spec.cr                         |   100 +-
 spec/network_spec.cr                     |   372 +-
 src/shainet/basic/data.cr                |    14 +-
 src/shainet/basic/es.cr                  |   129 +-
 src/shainet/basic/network.cr             |   114 +-
 src/shainet/cnn/cnn.cr                   |     3 +-
 src/shainet/{basic => math}/functions.cr |     0
 src/shainet/math/random_normal.cr        |    56 +
 10 files changed, 10463 insertions(+), 338 deletions(-)
 create mode 100644 csv_file.csv
 rename src/shainet/{basic => math}/functions.cr (100%)
 create mode 100644 src/shainet/math/random_normal.cr

diff --git a/README.md b/README.md
index 80ab8b6..fc10a35 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,11 @@
 
 
 SHAInet - stands for Super Human Artificial Intelligence network
-a neural network in pure [Crystal](https://crystal-lang.org/)  
+a neural network in pure [Crystal](https://crystal-lang.org/)
+
+This is a free-time project, happily hosted by NeuraLegion that was created as part of some internal research. We started it with research in mind, rather than production, and just kept going, also thanks to members of the community.
+
+We wanted to try and implement some inspiration from the biological world into this project. In addition to that, we wanted to try an approach for NNs using object-oriented modeling instead of matrices. The main reason behind that was, to try new types of neurons aiming for more robust learning (if possible) or at least have more fine-tuned control over the manipulation of each neuron (which is difficult using a matrix-driven approach).
 
 At the [Roadmap](https://github.com/NeuraLegion/shainet#development) you can see what we plan to add to the network as the project will progress.  
 
@@ -201,11 +205,13 @@ puts "Cnn output: #{cnn.output}"
   - [x] Add more activation functions.  
   - [x] Add more cost functions.  
   - [x] Add more gradient optimizers
-    - [x] Add iRprop+  
-    - [x] ADAM  
+    - [x] iRprop+  
+    - [x] ADAM
+    - [x] ES (evolutionary strategy, non-backprop)
 
 ### Advanced Features  
   - [x] Convolutional Neural Net.  
+  - [ ] Add support for multiple neuron types.  
   - [ ] Bind and use CUDA (GPU acceleration)  
   - [ ] graphic printout of network architecture.  
   - [ ] Add LSTM.  
@@ -213,7 +219,6 @@ puts "Cnn output: #{cnn.output}"
   - [ ] GNG (growing neural gas).  
   - [ ] SOM (self organizing maps).  
   - [ ] DBM (deep belief network).  
-  - [ ] Add support for multiple neuron types.  
 
 
 
diff --git a/csv_file.csv b/csv_file.csv
new file mode 100644
index 0000000..b3379d3
--- /dev/null
+++ b/csv_file.csv
@@ -0,0 +1,10000 @@
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diff --git a/spec/cnn_spec.cr b/spec/cnn_spec.cr
index 8493b2b..c4761af 100644
--- a/spec/cnn_spec.cr
+++ b/spec/cnn_spec.cr
@@ -134,18 +134,18 @@ describe SHAInet::CNN do
       padding: 2,
       activation_function: SHAInet.none) # Output shape = 28x28x20
     cnn.add_relu(0.01)                   # Output shape = 28x28x20
-    cnn.add_fconnect(l_size: 10, activation_function: SHAInet.sigmoid)
+    cnn.add_fconnect(l_size: 10, activation_function: SHAInet.none)
     cnn.add_softmax
 
-    cnn.learning_rate = 0.005
+    cnn.learning_rate = 0.05
     cnn.momentum = 0.02
 
     # cnn.run(test_data.data_pairs.first[:input], stealth = false)
     cnn.train_batch(
       data: training_data.data_pairs,
       training_type: :sgdm,
-      cost_function: :mse,
-      epochs: 3,
+      cost_function: :c_ent,
+      epochs: 10,
       error_threshold: 0.0001,
       log_each: 1,
       mini_batch_size: 50)
@@ -157,10 +157,100 @@ describe SHAInet::CNN do
         correct_answers += 1
       end
     end
-    cnn.inspect("activations")
+    # cnn.inspect("activations")
     puts "We managed #{correct_answers} out of #{test_data.data_pairs.size} total"
     puts "Cnn output: #{cnn.output}"
   end
+
+  # it "Figure out MNIST (mini-batch train, cross-entropy)" do
+  #   puts "Figure out MNIST (mini-batch train, cross-entropy)"
+  #   # Load training data (partial dataset)
+  #   raw_data = Array(Array(Float64)).new
+  #   csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_train.csv"))
+  #   1000.times do
+  #     # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
+  #     csv.next
+  #     new_row = Array(Float64).new
+  #     csv.row.to_a.each { |value| new_row << value.to_f64 }
+  #     raw_data << new_row
+  #   end
+  #   raw_input_data = Array(Array(Float64)).new
+  #   raw_output_data = Array(Array(Float64)).new
+
+  #   raw_data.each do |row|
+  #     raw_input_data << row[1..-1]
+  #     raw_output_data << [row[0]]
+  #   end
+
+  #   training_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)
+  #   training_data.for_mnist_conv
+  #   training_data.data_pairs.shuffle!
+
+  #   # puts "#{training_data.data_pairs.first[:output]}"
+  #   # training_data.data_pairs.first[:input].first.each do |row|
+  #   #   puts "#{row}"
+  #   # end
+
+  #   # Load test data (partial dataset)
+  #   raw_data = Array(Array(Float64)).new
+  #   csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_test.csv"))
+  #   100.times do
+  #     csv.next
+  #     new_row = Array(Float64).new
+  #     csv.row.to_a.each { |value| new_row << value.to_f64 }
+  #     raw_data << new_row
+  #   end
+
+  #   raw_input_data = Array(Array(Float64)).new
+  #   raw_output_data = Array(Array(Float64)).new
+
+  #   raw_data.each do |row|
+  #     raw_input_data << row[1..-1]
+  #     raw_output_data << [row[0]]
+  #   end
+
+  #   test_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)
+  #   test_data.for_mnist_conv
+
+  #   cnn = SHAInet::CNN.new
+  #   cnn.add_input([height = 28, width = 28, channels = 1]) # Output shape = 28x28x1
+  #   cnn.add_conv(
+  #     filters_num: 10,
+  #     window_size: 5,
+  #     stride: 1,
+  #     padding: 2,
+  #     activation_function: SHAInet.none) # Output shape = 28x28x20
+  #   cnn.add_relu(0.01)                   # Output shape = 28x28x20
+  #   cnn.add_fconnect(l_size: 10, activation_function: SHAInet.sigmoid)
+  #   cnn.add_softmax
+
+  #   cnn.learning_rate = 0.005
+  #   cnn.momentum = 0.02
+
+  #   # cnn.run(test_data.data_pairs.first[:input], stealth = false)
+
+  #   cnn.train_es(
+  #     data: training_data.data_pairs,
+  #     pool_size: 50,
+  #     learning_rate: 0.2,
+  #     sigma: 0.3,
+  #     cost_function: :c_ent,
+  #     epochs: 10,
+  #     mini_batch_size: 1,
+  #     error_threshold: 0.00001,
+  #     log_each: 1)
+
+  #   correct_answers = 0
+  #   test_data.data_pairs.each do |data_point|
+  #     result = cnn.run(data_point[:input], stealth: true)
+  #     if (result.index(result.max) == data_point[:output].index(data_point[:output].max))
+  #       correct_answers += 1
+  #     end
+  #   end
+  #   cnn.inspect("activations")
+  #   puts "We managed #{correct_answers} out of #{test_data.data_pairs.size} total"
+  #   puts "Cnn output: #{cnn.output}"
+  # end
 end
 
 # Remove train data
diff --git a/spec/network_spec.cr b/spec/network_spec.cr
index c1efeca..96a68c0 100644
--- a/spec/network_spec.cr
+++ b/spec/network_spec.cr
@@ -5,6 +5,27 @@ require "csv"
 system("cd #{__DIR__}/test_data && tar xvf tests.tar.xz")
 
 describe SHAInet::Network do
+  # it "Initialize" do
+  #   nn = SHAInet::Network.new
+  #   nn.should be_a(SHAInet::Network)
+  # end
+
+  # it "saves_to_file" do
+  #   nn = SHAInet::Network.new
+  #   nn.add_layer(:input, 2, :memory, SHAInet.sigmoid)
+  #   nn.add_layer(:output, 2, :memory, SHAInet.sigmoid)
+  #   nn.add_layer(:hidden, 2, :memory, SHAInet.sigmoid)
+  #   nn.fully_connect
+  #   nn.save_to_file("./my_net.nn")
+  #   File.exists?("./my_net.nn").should eq(true)
+  # end
+
+  # it "loads_from_file" do
+  #   nn = SHAInet::Network.new
+  #   nn.load_from_file("./my_net.nn")
+  #   (nn.all_neurons.size > 0).should eq(true)
+  # end
+
   # it "Test on a linear regression model" do
   #   # data structures to hold the input and results
   #   inputs = Array(Array(Float64)).new
@@ -48,27 +69,6 @@ describe SHAInet::Network do
   #   (error < 0.05).should eq(true) # require less than 5% error
   # end
 
-  # it "Initialize" do
-  #   nn = SHAInet::Network.new
-  #   nn.should be_a(SHAInet::Network)
-  # end
-
-  # it "saves_to_file" do
-  #   nn = SHAInet::Network.new
-  #   nn.add_layer(:input, 2, :memory, SHAInet.sigmoid)
-  #   nn.add_layer(:output, 2, :memory, SHAInet.sigmoid)
-  #   nn.add_layer(:hidden, 2, :memory, SHAInet.sigmoid)
-  #   nn.fully_connect
-  #   nn.save_to_file("./my_net.nn")
-  #   File.exists?("./my_net.nn").should eq(true)
-  # end
-
-  # it "loads_from_file" do
-  #   nn = SHAInet::Network.new
-  #   nn.load_from_file("./my_net.nn")
-  #   (nn.all_neurons.size > 0).should eq(true)
-  # end
-
   # it "Figure out XOR with SGD + M" do
   #   puts "---"
   #   puts "Figure out XOR SGD + momentum (train, mse, sigmoid)"
@@ -102,7 +102,7 @@ describe SHAInet::Network do
   #     pool_size: 100,
   #     learning_rate: 0.2,
   #     sigma: 0.3,
-  #     cost_function: :mse,
+  #     cost_function: :c_ent,
   #     epochs: 1000,
   #     mini_batch_size: 1,
   #     error_threshold: 0.0,
@@ -224,55 +224,55 @@ describe SHAInet::Network do
   #     training_type: :adam,
   #     cost_function: :mse,
   #     epochs: 20000,
-  #     error_threshold: 0.000001,
+  #     error_threshold: 0.00001,
   #     log_each: 1000)
 
   #   result = iris.run(normalized.normalized_inputs.first)
   #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   # end
 
-  it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
-    puts "---"
-    puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
-    label = {
-      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-    }
-    iris = SHAInet::Network.new
-    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-    iris.fully_connect
-
-    iris.learning_rate = 0.7
-    iris.momentum = 0.3
-
-    outputs = Array(Array(Float64)).new
-    inputs = Array(Array(Float64)).new
-    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-      row_arr = Array(Float64).new
-      row[0..-2].each do |num|
-        row_arr << num.to_f64
-      end
-      inputs << row_arr
-      outputs << label[row[-1]]
-    end
-    normalized = SHAInet::TrainingData.new(inputs, outputs)
-    normalized.normalize_min_max
-
-    iris.train_batch(
-      data: normalized.data.shuffle,
-      training_type: :adam,
-      cost_function: :mse,
-      epochs: 5000,
-      error_threshold: 0.000001,
-      mini_batch_size: 50,
-      log_each: 1000)
-
-    result = iris.run(normalized.normalized_inputs.first)
-    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
-  end
+  # it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
+  #   puts "---"
+  #   puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
+  #   label = {
+  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+  #   }
+  #   iris = SHAInet::Network.new
+  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+  #   iris.fully_connect
+
+  #   iris.learning_rate = 0.7
+  #   iris.momentum = 0.3
+
+  #   outputs = Array(Array(Float64)).new
+  #   inputs = Array(Array(Float64)).new
+  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+  #     row_arr = Array(Float64).new
+  #     row[0..-2].each do |num|
+  #       row_arr << num.to_f64
+  #     end
+  #     inputs << row_arr
+  #     outputs << label[row[-1]]
+  #   end
+  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
+  #   normalized.normalize_min_max
+
+  #   iris.train_batch(
+  #     data: normalized.data.shuffle,
+  #     training_type: :adam,
+  #     cost_function: :mse,
+  #     epochs: 5000,
+  #     error_threshold: 0.000001,
+  #     mini_batch_size: 50,
+  #     log_each: 1000)
+
+  #   result = iris.run(normalized.normalized_inputs.first)
+  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
+  # end
 
   # it "trains , saves, loads, runs" do
   #   puts "---"
@@ -366,121 +366,161 @@ describe SHAInet::Network do
   #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
   # end
 
-  it "works on iris dataset using evolutionary strategies as optimizer" do
-    puts "---"
-    puts "works on iris dataset using evolutionary strategies as optimizer"
-    label = {
-      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-    }
-    iris = SHAInet::Network.new
-    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-    iris.fully_connect
-
-    outputs = Array(Array(Float64)).new
-    inputs = Array(Array(Float64)).new
-    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-      row_arr = Array(Float64).new
-      row[0..-2].each do |num|
-        row_arr << num.to_f64
-      end
-      inputs << row_arr
-      outputs << label[row[-1]]
-    end
-    normalized = SHAInet::TrainingData.new(inputs, outputs)
-    normalized.normalize_min_max
-
-    iris.train_es(
-      data: normalized.data.shuffle,
-      pool_size: 50,
-      learning_rate: 0.2,
-      sigma: 0.3,
-      cost_function: :c_ent,
-      epochs: 100,
-      mini_batch_size: 1,
-      error_threshold: 0.00001,
-      log_each: 1)
-
-    result = iris.run(normalized.normalized_inputs.first)
-    expected = [0.0, 0.0, 1.0]
-    puts "result: \t#{result.map { |x| x.round(5) }}"
-    puts "expected: \t#{expected}"
-    ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
-  end
-
-  # it "works on the mnist dataset using adam and batch" do
-  #   mnist = SHAInet::Network.new
-  #   mnist.add_layer(:input, 784, "memory", SHAInet.sigmoid)
-  #   mnist.add_layer(:hidden, 100, "memory", SHAInet.sigmoid)
-  #   mnist.add_layer(:hidden, 40, "eraser", SHAInet.sigmoid)
-  #   mnist.add_layer(:hidden, 40, "memory", SHAInet.sigmoid)
-  #   mnist.add_layer(:hidden, 100, "memory", SHAInet.sigmoid)
-  #   mnist.add_layer(:output, 10, "memory", SHAInet.sigmoid)
-
-  #   # Input to first hidden
-  #   mnist.connect_ltl(mnist.input_layers.first, mnist.hidden_layers.first, :full)
-
-  #   # first hidden to [1] and [2]
-  #   mnist.connect_ltl(mnist.hidden_layers.first, mnist.hidden_layers[1], :full)
-  #   mnist.connect_ltl(mnist.hidden_layers.first, mnist.hidden_layers[2], :full)
-
-  #   # [1] and [2] to last hidden
-  #   mnist.connect_ltl(mnist.hidden_layers[1], mnist.hidden_layers.last, :full)
-  #   mnist.connect_ltl(mnist.hidden_layers[2], mnist.hidden_layers.last, :full)
+  # it "works on iris dataset using evolutionary strategies as optimizer + cross-entropy" do
+  #   puts "---"
+  #   # puts "works on iris dataset using evolutionary strategies as optimizer + cross-entropy"
+  #   label = {
+  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+  #   }
 
-  #   # [0] & [3] to output
-  #   mnist.connect_ltl(mnist.hidden_layers[0], mnist.output_layers.first, :full)
-  #   mnist.connect_ltl(mnist.hidden_layers[3], mnist.output_layers.first, :full)
+  #   iris = SHAInet::Network.new
+  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+  #   iris.fully_connect
 
-  #   # Load train data
   #   outputs = Array(Array(Float64)).new
   #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
+  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
   #     row_arr = Array(Float64).new
-  #     row[1..-1].each do |num|
+  #     row[0..-2].each do |num|
   #       row_arr << num.to_f64
   #     end
   #     inputs << row_arr
-  #     a = Array(Float64).new(10, 0.0)
-  #     a[row[0].to_i] = 1.0
-  #     outputs << a
+  #     outputs << label[row[-1]]
   #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
-  #   # Train on the data
-  #   mnist.train_batch(normalized.data.shuffle, :adam, :mse, 100, 0.0035, 10, 10000)
+  #   data = SHAInet::TrainingData.new(inputs, outputs)
+  #   data.normalize_min_max
 
-  #   # Load test data
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   results = Array(Int32).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/mnist_test.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[1..-1].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     a = Array(Float64).new(10, 0.0)
-  #     a[row[0].to_i] = 1.0
-  #     outputs << a
-  #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
-  #   # Run on all test data, and see that we are atleast 0.01 far from the right solution
-  #   normalized.normalized_inputs.each_with_index do |test, i|
-  #     result = mnist.run(test, stealth = true)
-  #     if (result.index(result.max) == normalized.normalized_outputs[i].index(normalized.normalized_outputs[i].max))
-  #       results << 1
-  #     else
-  #       results << 0
+  #   training_data, test_data = data.split(0.9)
+
+  #   iris.train_es(
+  #     data: training_data,
+  #     pool_size: 50,
+  #     learning_rate: 0.5,
+  #     sigma: 0.1,
+  #     cost_function: :c_ent,
+  #     epochs: 500,
+  #     mini_batch_size: 15,
+  #     error_threshold: 0.00000001,
+  #     log_each: 100,
+  #     show_slice: true)
+
+  #   # Test the trained model
+  #   correct = 0
+  #   test_data.data.each do |data_point|
+  #     result = iris.run(data_point[0], stealth: true)
+  #     expected = data_point[1]
+  #     # puts "result: \t#{result.map { |x| x.round(5) }}"
+  #     # puts "expected: \t#{expected}"
+  #     error_sum = 0.0
+  #     result.size.times do |i|
+  #       error_sum += (result[i] - expected[i]).abs
   #     end
+  #     correct += 1 if error_sum < 0.3
   #   end
-  #   puts "We managed #{results.sum} out of #{results.size} total"
+  #   puts "Correct answers: (#{correct} / #{test_data.size})"
+  #   (correct > 10).should eq(true)
   # end
 
+  it "works on the mnist dataset using evolutionary optimizer and batch" do
+    mnist = SHAInet::Network.new
+    mnist.add_layer(:input, 784, "memory", SHAInet.sigmoid)
+    mnist.add_layer(:hidden, 50, "memory", SHAInet.sigmoid)
+    # mnist.add_layer(:hidden, 40, "eraser", SHAInet.sigmoid)
+    # mnist.add_layer(:hidden, 10, "memory", SHAInet.sigmoid)
+    # mnist.add_layer(:hidden, 100, "memory", SHAInet.sigmoid)
+    mnist.add_layer(:output, 10, "memory", SHAInet.sigmoid)
+
+    # Input to first hidden
+    mnist.fully_connect
+
+    # Load training data
+    raw_data = Array(Array(Float64)).new
+    csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_train.csv"))
+    10000.times do
+      # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
+      csv.next
+      new_row = Array(Float64).new
+      csv.row.to_a.each { |value| new_row << value.to_f64 }
+      raw_data << new_row
+    end
+    raw_input_data = Array(Array(Float64)).new
+    raw_output_data = Array(Array(Float64)).new
+
+    raw_data.each do |row|
+      raw_input_data << row[1..-1]
+      raw_output_data << [row[0]]
+    end
+
+    # Change digits to one-hot vectors
+    raw_output_data.each_with_index do |point, i|
+      lbl = point.first.clone.to_i
+      one_hot = Array(Float64).new(10) { 0.0 }
+      one_hot[lbl] = 1.0
+      raw_output_data[i] = one_hot
+    end
+
+    training_data = SHAInet::TrainingData.new(raw_input_data, raw_output_data)
+    training_data.normalize_min_max
+
+    # Train on the data
+    mnist.train_es(
+      data: training_data,
+      pool_size: 50,
+      learning_rate: 0.5,
+      sigma: 0.1,
+      cost_function: :c_ent,
+      epochs: 10,
+      mini_batch_size: 100,
+      error_threshold: 0.00000001,
+      log_each: 10,
+      show_slice: true)
+
+    # Load test data
+    raw_data = Array(Array(Float64)).new
+    csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_test.csv"))
+    1000.times do
+      # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
+      csv.next
+      new_row = Array(Float64).new
+      csv.row.to_a.each { |value| new_row << value.to_f64 }
+      raw_data << new_row
+    end
+    raw_input_data = Array(Array(Float64)).new
+    raw_output_data = Array(Array(Float64)).new
+
+    # Change digits to one-hot vectors
+    raw_data.each do |row|
+      raw_input_data << row[1..-1]
+      raw_output_data << [row[0]]
+    end
+
+    raw_output_data.each_with_index do |point, i|
+      lbl = point.first.clone.to_i
+      one_hot = Array(Float64).new(10) { 0.0 }
+      one_hot[lbl] = 1.0
+      raw_output_data[i] = one_hot
+    end
+
+    test_data = SHAInet::TrainingData.new(raw_input_data, raw_output_data)
+    test_data.normalize_min_max
+
+    # Run on all test data
+    results = Array(Int32).new
+    test_data.normalized_inputs.each_with_index do |test, i|
+      result = mnist.run(input: test, stealth: true)
+      if (result.index(result.max) == test_data.normalized_outputs[i].index(test_data.normalized_outputs[i].max))
+        results << 1
+      else
+        results << 0
+      end
+    end
+    puts "We managed #{results.sum} out of #{results.size} total"
+  end
 end
+
 # Remove train data
 system("cd #{__DIR__}/test_data && rm *.csv")
diff --git a/src/shainet/basic/data.cr b/src/shainet/basic/data.cr
index f43d01f..14381c8 100644
--- a/src/shainet/basic/data.cr
+++ b/src/shainet/basic/data.cr
@@ -7,6 +7,7 @@ module SHAInet
     @ymin : Int32
 
     getter :normalized_outputs, :normalized_inputs, :labels
+    getter :inputs, :outputs
     setter :outputs
 
     # @data_pairs :
@@ -64,7 +65,7 @@ module SHAInet
       @inputs.each_with_index do |i_arr, i|
         arr << [@inputs[i], @outputs[i]]
       end
-      arr
+      return arr
     end
 
     def normalize_min_max
@@ -128,10 +129,17 @@ module SHAInet
     # Splits the receiver in a TrainingData and a TestData object according to factor
     def split(factor)
       training_set_size = (data.size * factor).to_i
+
+      # puts data
       shuffled_data = data.shuffle
+
+      # puts shuffled_data
+
       training_set = shuffled_data[0..training_set_size - 1]
       test_set = shuffled_data[training_set_size..shuffled_data.size - 1]
 
+      # puts training_set
+
       @logger.info "Selected #{training_set.size} / #{data.size} rows for training"
       training_data = SHAInet::TrainingData.new(training_set.map { |el| el[0] }, training_set.map { |el| el[1] })
       training_data.labels = @labels
@@ -161,5 +169,9 @@ module SHAInet
       index = an_array.index(an_array.max.to_f64)
       index ? @labels[index] : ""
     end
+
+    def size
+      return @inputs.size
+    end
   end
 end
diff --git a/src/shainet/basic/es.cr b/src/shainet/basic/es.cr
index 5a87903..5b52a9a 100644
--- a/src/shainet/basic/es.cr
+++ b/src/shainet/basic/es.cr
@@ -3,11 +3,9 @@
 
 module SHAInet
   class Pool
-    property :mvp
-    getter :organisms
+    getter organisms : Array(Organism), pool_biases : Array(Float64), pool_weights : Array(Float64)
 
-    @organisms : Array(Organism)
-    @mvp : Organism
+    # property mvp : Organism
 
     def initialize(@network : Network,
                    @pool_size : Int32,
@@ -15,13 +13,13 @@ module SHAInet
                    @sigma : Float64)
       #
       raise "Pool size must be at least 2" if @pool_size < 2
+
       # Store previous data to avoid moving towards worse network states
       @pool_biases = Array(Float64).new
-      @pool_weights = Array(Float64).new
-      save_nn_params
+      @network.all_neurons.each { |neuron| @pool_biases << neuron.bias }
 
-      # @pool_biases = Array(Float64).new
-      # @pool_weights = Array(Float64).new
+      @pool_weights = Array(Float64).new
+      @network.all_synapses.each { |synapse| @pool_weights << synapse.weight }
 
       @organisms = Array(Organism).new
       @pool_size.times do
@@ -33,21 +31,9 @@ module SHAInet
           original_weights: @pool_weights
         )
       end
-      @mvp = @organisms.sample.as(Organism)
+      # @mvp = @organisms.sample.as(Organism)
     end
 
-    def save_nn_params
-      # @pool_biases = Array(Float64).new
-      # @pool_weights = Array(Float64).new
-      @network.all_neurons.each { |neuron| @pool_biases << neuron.bias }
-      @network.all_synapses.each { |synapse| @pool_weights << synapse.weight }
-    end
-
-    # def restore_nn_params
-    #   @network.all_neurons.each_with_index { |neuron, i| neuron.bias = @pool_biases[i] }
-    #   @network.all_synapses.each_with_index { |synapse, i| synapse.weight = @pool_weights[i] }
-    # end
-
     def normalize_rewards
       reward_mean = 0.0
       reward_stdv = 0.0
@@ -60,7 +46,7 @@ module SHAInet
 
       # Calculate standard deviation
       @organisms.each do |organism|
-        reward_stdv += Math.sqrt((organism.reward - reward_mean)**2 / (@organisms.size - 1))
+        reward_stdv += ((organism.reward - reward_mean)**2 / (@organisms.size - 1))**0.5
       end
 
       @organisms.each do |organism|
@@ -73,22 +59,26 @@ module SHAInet
       norm_value = @learning_rate / (@pool_size * @sigma)
       # puts "norm_value: #{norm_value}"
 
+      # Sum the relative change each organism provides
       @organisms.each do |organism|
         organism.biases.each_with_index do |bias, i|
           weighted_value = bias * organism.reward
-          @pool_biases[i] += weighted_value # norm_value * weighted_value
+          @pool_biases[i] += norm_value * weighted_value
           # puts "i: #{i}"
           # puts "organism.error_signal: #{organism.error_signal}"
           # puts "organism.reward: #{organism.reward}"
           # puts "bias: #{bias}"
           # puts "weighted_value: #{weighted_value}"
         end
+
         organism.weights.each_with_index do |weight, i|
           weighted_value = weight * organism.reward
-          @pool_weights[i] += weighted_value # norm_value * weighted_value
+          @pool_weights[i] += norm_value * weighted_value
         end
       end
 
+      # puts "@pool_biases: #{@pool_biases}"
+
       # Update network biases
       @network.all_neurons.each_with_index do |neuron, i|
         neuron.bias = @pool_biases[i].clone
@@ -99,36 +89,11 @@ module SHAInet
         synapse.weight = @pool_weights[i].clone
       end
     end
-
-    # def reset
-    #   @organisms = Array(Organism).new
-    #   @pool_size.times do
-    #     @organisms << Organism.new(
-    #       network: @network,
-    #       learning_rate: @learning_rate,
-    #       sigma: @sigma,
-    #       original_biases: @original_biases,
-    #       original_weights: @original_weights
-    #     )
-    #   end
-    # end
-
-    # def natural_select(error_threshold : Float64)
-    #   @original_biases = @mvp.original_biases.clone
-    #   @original_weights = @mvp.original_weights.clone
-
-    #   @organisms.each do |organism|
-    #     if organism.mse > error_threshold
-    #       organism.reset(@original_biases, @original_weights)
-    #     else
-    #       organism.update_params(@original_biases, @original_weights)
-    #     end
-    #   end
-    # end
   end
 
   class Organism
-    property mse : Float64, error_signal : Array(Float64), reward : Float64
+    property reward : Float64
+    getter mse : Float64, error_signal : Array(Float64)
     getter biases : Array(Float64), weights : Array(Float64)
 
     @network : Network
@@ -155,81 +120,43 @@ module SHAInet
       @weights = original_weights.clone
     end
 
-    # def reset(original_biases : Array(Float64), original_weights : Array(Float64))
-    #   @learning_rate = rand(0.0..1.0)
-    #   @sigma = rand(0.0..1.0)
-    #   @mse = 100000.0
-    #   @original_biases = original_biases
-    #   @original_weights = original_weights
-    # end
-
-    # def update_params(original_biases : Array(Float64), original_weights : Array(Float64))
-    #   @original_biases = original_biases
-    #   @original_weights = original_weights
-    # end
-
     def get_new_params
       # Update biases
       @network.all_neurons.each_with_index do |neuron, i|
-        # Only change value if mutation is triggered
-        # This alows for some of the values to remain between epochs
+        # threshold = @sigma*(neuron.bias.clone).abs
+        # change = rand(-threshold..threshold) # Add noise
 
-        # if rand(0.0..1.0) < @sigma
-        # Update networks biases using the organisms specific parameters
-        # threshold = (@learning_rate*@original_biases[i]).abs
+        # change = rand(-@sigma..@sigma) # Add noise
+        # new_value = @original_biases[i].clone + change*@learning_rate
 
-        change = rand(-@sigma..@sigma) # Add noise
-        new_value = @original_biases[i] + change*@learning_rate
+        new_value = SHAInet::RandomNormal.sample(n: 1, mu: neuron.bias, sigma: @sigma).first
+        # puts "new_value: #{new_value}"
         neuron.bias = new_value.clone
         @biases[i] = new_value.clone
-
-        # end
       end
 
       # Update weights
       @network.all_synapses.each_with_index do |synapse, i|
-        # Only change value if mutation is triggered
-        # This alows for some of the values to remain between epochs
-        # if rand(0.0..1.0) < @sigma
-        # Update networks biases using the organisms specific parameters
-        # threshold = (@learning_rate*@original_weights[i]).abs
+        # threshold = @sigma*(synapse.weight.clone).abs
+        # change = rand(-threshold..threshold) # Add noise
+        # # change = rand(-@sigma..@sigma) # Add noise
+        # new_value = @original_weights[i].clone + change*@learning_rate
 
-        change = rand(-@sigma..@sigma) # Add noise
-
-        # change = rand(-threshold..threshold)
-        new_value = @original_weights[i] + change*@learning_rate
+        new_value = SHAInet::RandomNormal.sample(n: 1, mu: synapse.weight, sigma: @sigma).first
         synapse.weight = new_value
         @weights[i] = new_value
-        # end
       end
     end
 
-    # def pull_params
-    #   # Update biases
-    #   @network.all_neurons.each_with_index do |neuron, i|
-    #     neuron.bias = @biases[i].clone
-    #   end
-
-    #   # Update weights
-    #   @network.all_synapses.each_with_index do |synapse, i|
-    #     synapse.weight = @weights[i].clone
-    #   end
-    # end
-
     def update_reward
       @error_signal = @network.error_signal.clone
       @reward = 0.0
       @error_signal.each { |v| @reward -= v }
-      # reward_sum = -@error_signal.reduce(0.0) { |acc, i| acc + i }
-      # @reward = SHAInet._tanh(reward_sum)
 
-      # puts "@reward: #{@reward}"
-      # puts "@error_signal: #{@error_signal}"
-      # @reward = -@mse.clone # ((@network.prev_mse - @mse) / @network.prev_mse)
       # puts "###############"
       # puts "@network.prev_mse: #{@network.prev_mse}"
       # puts "@mse: #{@mse}"
-      # puts "reward: #{@reward}"
+      # puts "@reward: #{@reward}"
       # puts "###############"
     end
   end
diff --git a/src/shainet/basic/network.cr b/src/shainet/basic/network.cr
index ffbfb79..8a5faaf 100644
--- a/src/shainet/basic/network.cr
+++ b/src/shainet/basic/network.cr
@@ -371,19 +371,20 @@ module SHAInet
         cost_function = proc
       end
 
-      raw_data.each_slice(batch_size, reuse = false) do |data_slice|
-        verify_data(data_slice)
-        @logger.info("Working on mini-batch size: #{batch_size}") if mini_batch_size
-        @time_step += 1 if mini_batch_size # in mini-batch update adam time_step
-        loop do |e|
-          if e % log_each == 0
-            log_summary(e)
-            # @all_neurons.each { |s| puts s.gradient }
-          end
-          if e >= epochs || (error_threshold >= @mse) && (e > 1)
-            log_summary(e)
-            break
-          end
+      loop do |e|
+        if e % log_each == 0
+          log_summary(e)
+          # @all_neurons.each { |s| puts s.gradient }
+        end
+        if e >= epochs || (error_threshold >= @mse) && (e > 1)
+          log_summary(e)
+          break
+        end
+
+        raw_data.each_slice(batch_size, reuse = false) do |data_slice|
+          verify_data(data_slice)
+          # @logger.info("Working on mini-batch size: #{batch_size}") if mini_batch_size
+          @time_step += 1 if mini_batch_size # in mini-batch update adam time_step
 
           batch_mean = [] of Float64
           all_errors = [] of Float64
@@ -557,7 +558,8 @@ module SHAInet
                  epochs : Int32 = 1,                                         # a criteria of when to stop the training
                  mini_batch_size : Int32 = 1,                                # Size of batch
                  error_threshold : Float64 = 0.0,                            # a criteria of when to stop the training
-                 log_each : Int32 = 1)                                       # determines what is the step for error printout
+                 log_each : Int32 = 1,                                       # determines what is the step for error printout
+                 show_slice : Bool = false)                                  # Show MSE for each slice
       # This methods accepts data as either a SHAInet::TrainingData object, or as an Array(Array(Array(GenNum)).
       # In the case of SHAInet::TrainingData, we convert it to an Array(Array(Array(GenNum)) by calling #data on it.
       raw_data = data.is_a?(SHAInet::TrainingData) ? data.data : data
@@ -572,15 +574,24 @@ module SHAInet
       end
 
       loop do |e|
-        if e % log_each == 0
-          log_summary(e)
-          # @all_neurons.each { |s| puts s.gradient }
-        end
         if e >= epochs || (error_threshold >= @mse) && (e > 1)
           log_summary(e)
           break
         end
 
+        # Show training progress of epochs
+        if e % log_each == 0
+          log_summary(e)
+        end
+
+        # Counters for disply
+        i = 0
+        slices = data.size / mini_batch_size
+
+        # Temp
+        biases = [] of Float64
+        weights = [] of Float64
+
         raw_data.each_slice(batch_size, reuse = false) do |data_slice|
           verify_data(data_slice)
 
@@ -589,54 +600,37 @@ module SHAInet
             pool_size: pool_size,
             learning_rate: learning_rate,
             sigma: sigma)
-          pool.save_nn_params
-          # @logger.info("Working on mini-batch size: #{batch_size}") if mini_batch_size
 
           # Update wieghts & biases for the batch
           pool.organisms.each do |organism|
             organism.get_new_params # Get new weights & biases
 
-            # Go over each data points and collect mse
+            # Go over each data points and collect errors
             # based on each specific example in the batch
             batch_mse_sum = 0.0
-            batch_err_sig_sum = Array(Float64).new(@output_layers.last.neurons.size) { 0.0 }
+            batch_errors_sum = Array(Float64).new(@output_layers.last.neurons.size) { 0.0 }
 
             data_slice.each do |data_point|
-              evaluate(data_point[0], data_point[1], cost_function) # Update error signal in output layer
+              # Update error signal in output layer
+              evaluate(data_point[0], data_point[1], cost_function)
               update_mse
               batch_mse_sum += @mse
-              @error_signal.size.times { |i| batch_err_sig_sum[i] += @error_signal[i] }
-
-              # puts "data_point: #{data_point}"
-              # puts "@error_signal: #{@error_signal}"
+              @error_signal.size.times { |i| batch_errors_sum[i] += @error_signal[i] }
             end
 
-            @mse = batch_mse_sum / mini_batch_size # Update MSE of the batch
-            # organism.mse
+            @mse = (batch_mse_sum / mini_batch_size) # Update MSE of the batch
+            @error_signal = batch_errors_sum
             organism.update_reward
-
-            #   if organism.mse < pool.mvp.mse
-            #     pool.mvp = organism
-            #   end
           end
 
           pool.pull_params
 
-          # if pool.mvp.mse < @prev_mse
-          #   # Get the best parameters from the pool
-          #   pool.mvp.pull_params
-          #   @mse = pool.mvp.mse
-          #   @prev_mse = @mse.clone
-
-          #   puts "improved!"
-
-          #   # # Update pool for the next batch
-          #   # pool.natural_select(@prev_mse)
-          # else
-          #   # Try new pool on next batch
-          #   pool.restore_nn_params
-          #   # pool.reset
-          # end
+          # Show training progress of the mini-batches
+          i += 1
+          if e % log_each == 0
+            @logger.info("Slice: (#{i} / #{slices}), MSE: #{@mse}") if show_slice
+            # @logger.info("@error_signal: #{@error_signal}")
+          end
         end
       end
     end
@@ -651,6 +645,18 @@ module SHAInet
       @all_neurons.each &.randomize_bias
     end
 
+    def get_cost_proc(function_name : String) : CostFunction
+      case function_name
+      when "mse"
+        return SHAInet.quadratic_cost
+      when "c_ent"
+        # raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
+        return SHAInet.cross_entropy_cost
+      else
+        raise NeuralNetInitalizationError.new("Must choose correct cost function or provide a correct Proc")
+      end
+    end
+
     def save_to_file(file_path : String)
       dump_network = Array(Hash(String, String | Array(Hash(String, Array(Hash(String, String | Float64)) | Float64 | String | String)))).new
 
@@ -747,18 +753,6 @@ module SHAInet
       @logger.info("Network loaded from: #{file_path}")
     end
 
-    def get_cost_proc(function_name : String) : CostFunction
-      case function_name
-      when "mse"
-        return SHAInet.quadratic_cost
-      when "c_ent"
-        # raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
-        return SHAInet.cross_entropy_cost
-      else
-        raise NeuralNetInitalizationError.new("Must choose correct cost function or provide a correct Proc")
-      end
-    end
-
     def inspect
       @logger.info(@input_layers)
       @logger.info("--------------------------------")
diff --git a/src/shainet/cnn/cnn.cr b/src/shainet/cnn/cnn.cr
index a3c6c37..5a8e663 100644
--- a/src/shainet/cnn/cnn.cr
+++ b/src/shainet/cnn/cnn.cr
@@ -348,7 +348,8 @@ module SHAInet
       when "mse"
         return SHAInet.quadratic_cost
       when "c_ent"
-        raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
+        # raise MathError.new("Cross entropy cost is not implemented fully yet, please use quadratic cost for now.")
+        return SHAInet.cross_entropy_cost
       else
         raise NeuralNetInitalizationError.new("Must choose correct cost function or provide a correct Proc")
       end
diff --git a/src/shainet/basic/functions.cr b/src/shainet/math/functions.cr
similarity index 100%
rename from src/shainet/basic/functions.cr
rename to src/shainet/math/functions.cr
diff --git a/src/shainet/math/random_normal.cr b/src/shainet/math/random_normal.cr
new file mode 100644
index 0000000..0e7b98e
--- /dev/null
+++ b/src/shainet/math/random_normal.cr
@@ -0,0 +1,56 @@
+require "csv"
+
+module SHAInet
+  module RandomNormal
+    extend self
+
+    # Normal probability density function calculation
+    def pdf(x : Float64, mu : Float64, sigma : Float64)
+      max_y = Float64.new((1 / Math.sqrt(2 * Math::PI * sigma**2)))
+      exp = Float64.new(Math::E**(-1 * (x - mu)**2 / (2 * sigma**2)))
+      return max_y*exp
+    end
+
+    # Sampling n points from a normal distribution with mu & sigma,
+    # using the Metropolis-Hastings algorithm
+    def metropolis(n : Int32 = 1, mu : Float64 = 0.0, sigma : Float64 = 1.0)
+      points = Array(Float64).new
+      r = mu
+      p = pdf(x: r, mu: mu, sigma: sigma)
+
+      n.times do
+        rn = r.clone + rand(-1.0..1.0)
+        pn = pdf(x: rn, mu: mu, sigma: sigma)
+
+        if pn >= p
+          p = pn.clone
+          r = rn.clone
+        else
+          u = rand(1.0)
+          if u < (pn / p)
+            p = pn.clone
+            r = rn.clone
+          end
+        end
+        points << r
+      end
+
+      return points
+    end
+
+    # alias_method :sample, :metropolis
+    def sample(n : Int32 = 1, mu : Float64 = 0.0, sigma : Float64 = 1.0)
+      raise "Parameter error, sampling must be of n >= 1" if n < 1
+      return metropolis(n: n, mu: mu, sigma: sigma)
+    end
+  end
+end
+
+data = SHAInet::RandomNormal.sample(10000, 0.0, 3.0)
+csv_file = CSV.build do |csv|
+  data.each do |value|
+    csv.row value
+  end
+end
+
+File.write("csv_file.csv", csv_file)

From caa16775582557d455f3f42168a4854931246bdd Mon Sep 17 00:00:00 2001
From: art <artlinkov@gmail.com>
Date: Thu, 14 Jun 2018 17:20:40 +0300
Subject: [PATCH 8/8] Added example in README

---
 README.md                 |    60 +
 csv_file.csv              | 20000 ++++++++++++++++++------------------
 spec/cnn_spec.cr          |   170 +-
 spec/network_spec.cr      |   988 +-
 src/shainet/basic/data.cr |    29 +-
 src/shainet/basic/es.cr   |    15 -
 6 files changed, 10688 insertions(+), 10574 deletions(-)

diff --git a/README.md b/README.md
index fc10a35..4c71548 100644
--- a/README.md
+++ b/README.md
@@ -198,6 +198,66 @@ puts "We managed #{correct_answers} out of #{test_data.data_pairs.size} total"
 puts "Cnn output: #{cnn.output}"
 ```
 
+Evolutionary optimizer example:
+```crystal
+label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    # Get data from a local file
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
+
+    training_data, test_data = data.split(0.9)
+
+    iris.train_es(
+      data: training_data,
+      pool_size: 50,
+      learning_rate: 0.5,
+      sigma: 0.1,
+      cost_function: :c_ent,
+      epochs: 500,
+      mini_batch_size: 15,
+      error_threshold: 0.00000001,
+      log_each: 100,
+      show_slice: true)
+
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
+    end
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+```
+
+
 ## Development
 
 ### Basic Features  
diff --git a/csv_file.csv b/csv_file.csv
index b3379d3..c1ecb0a 100644
--- a/csv_file.csv
+++ b/csv_file.csv
@@ -1,10000 +1,10000 @@
--0.4682262520681676
--1.1971849657553704
--2.1467975078344224
--2.4513424505503174
--1.5111943652880844
--1.5721972022924078
--1.6939442021402606
--2.3756701820080326
--2.2801784226649993
--2.0980955476899377
--1.1212747016794145
--0.8212877800582417
--0.050461739550796025
--0.7742242653099529
--1.3678518442642613
--0.4160848298580788
-0.4973405234083428
-0.24291438720119773
--0.38307001662033746
--1.3361281845441981
--0.8393997545728755
--0.7957488575729759
--0.9657126472604242
--1.6902851536370642
--2.587587451808786
--2.5448363668227123
--1.6826299819759032
--2.640135564677026
--2.6861855497144815
--3.5033871812223403
--2.833576915220646
--3.1169799330259145
--2.8833721999618414
--2.354753167244263
--1.5174724975233504
--2.4715774663271994
--2.071651016087608
--2.241739355984376
--1.4457990245527002
--0.479711587649327
-0.43017509583249813
-0.591355896809824
-1.445217748646929
-1.4724133238954191
-1.414860548820828
-0.8664647055999155
-0.8008998101580703
-0.7691169286491781
-0.6659468982795945
-0.30801149915098835
-0.49437796394148936
--0.40062684287356776
--1.2244474804435244
--1.998829646450212
--1.998829646450212
--2.0278564898922085
--1.4917016461460435
--0.720282136482036
--0.8267819286395964
--0.3770825430020689
--0.34237436423380674
-0.1784989634996451
-0.009302652440343895
--0.249325636974162
-0.24159433922958018
-0.3215799697713747
-0.386730689428047
--0.44670643414616873
--0.11089974759582744
--0.1850090161194582
-0.7230954594212053
-0.06421348743334732
-0.7679962667778966
-1.5760589022722573
-1.5760589022722573
-1.5760589022722573
-2.5295040337036623
-2.8447969870206933
-2.895861212045367
-2.828146530235074
-2.671335810328856
-3.10453650744235
-3.9589891635419745
-3.5787599479493943
-4.036963999173473
-4.036963999173473
-3.872376356379685
-3.770701445992524
-3.862636091274476
-4.00424876642141
-3.3582935020601843
-2.637254144740516
-2.637254144740516
-2.250570163914073
-2.9060775267941255
-2.7241565658282902
-2.7241565658282902
-2.8813685996138645
-2.3133144620721273
-2.3133144620721273
-2.6732157772887932
-3.631899036053071
-4.224142059385554
-4.224142059385554
-3.6731741272557485
-3.6731741272557485
-3.3319144489749464
-2.8766301092459803
-2.453148531301357
-2.149059907173606
-1.9287064606299777
-1.9383013310130812
-1.139471058505917
-1.6553942380895987
-1.2756253013349728
-0.8164443601563479
-1.430375947377811
-2.18233617425493
-2.3257112200688024
-2.3257112200688024
-1.6549394895951128
-1.6549394895951128
-1.4193342313108683
-1.1016323600795719
-0.1852531701372533
--0.3379946890509189
--0.41475049483409177
-0.5030644021896845
-0.42765147742728704
-1.0519533203918598
-0.2437705699332935
--0.2839531095542498
--0.8348491921239369
--1.5690343415708332
--1.2845407456357625
--1.3033441844186657
--1.6845697740213281
--1.355237273141728
--1.4978406726931528
--0.7029188736135425
--0.2479770161466699
--0.1314547773542445
--0.739556615445375
--1.3031464794559777
--1.0903550348844004
--0.9705695761865496
--1.4906631133888102
--1.8669511769027611
--1.5654893102710143
--1.6375309612299622
--2.4125636616821415
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diff --git a/spec/cnn_spec.cr b/spec/cnn_spec.cr
index c4761af..5cadfb4 100644
--- a/spec/cnn_spec.cr
+++ b/spec/cnn_spec.cr
@@ -76,91 +76,91 @@ describe SHAInet::CNN do
   # end
   #
 
-  it "Figure out MNIST (mini-batch train, mse)" do
-    # Load training data (partial dataset)
-    raw_data = Array(Array(Float64)).new
-    csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_train.csv"))
-    1000.times do
-      # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
-      csv.next
-      new_row = Array(Float64).new
-      csv.row.to_a.each { |value| new_row << value.to_f64 }
-      raw_data << new_row
-    end
-    raw_input_data = Array(Array(Float64)).new
-    raw_output_data = Array(Array(Float64)).new
-
-    raw_data.each do |row|
-      raw_input_data << row[1..-1]
-      raw_output_data << [row[0]]
-    end
-
-    training_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)
-    training_data.for_mnist_conv
-    training_data.data_pairs.shuffle!
-
-    # puts "#{training_data.data_pairs.first[:output]}"
-    # training_data.data_pairs.first[:input].first.each do |row|
-    #   puts "#{row}"
-    # end
-
-    # Load test data (partial dataset)
-    raw_data = Array(Array(Float64)).new
-    csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_test.csv"))
-    100.times do
-      csv.next
-      new_row = Array(Float64).new
-      csv.row.to_a.each { |value| new_row << value.to_f64 }
-      raw_data << new_row
-    end
-
-    raw_input_data = Array(Array(Float64)).new
-    raw_output_data = Array(Array(Float64)).new
-
-    raw_data.each do |row|
-      raw_input_data << row[1..-1]
-      raw_output_data << [row[0]]
-    end
-
-    test_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)
-    test_data.for_mnist_conv
-
-    cnn = SHAInet::CNN.new
-    cnn.add_input([height = 28, width = 28, channels = 1]) # Output shape = 28x28x1
-    cnn.add_conv(
-      filters_num: 10,
-      window_size: 5,
-      stride: 1,
-      padding: 2,
-      activation_function: SHAInet.none) # Output shape = 28x28x20
-    cnn.add_relu(0.01)                   # Output shape = 28x28x20
-    cnn.add_fconnect(l_size: 10, activation_function: SHAInet.none)
-    cnn.add_softmax
-
-    cnn.learning_rate = 0.05
-    cnn.momentum = 0.02
-
-    # cnn.run(test_data.data_pairs.first[:input], stealth = false)
-    cnn.train_batch(
-      data: training_data.data_pairs,
-      training_type: :sgdm,
-      cost_function: :c_ent,
-      epochs: 10,
-      error_threshold: 0.0001,
-      log_each: 1,
-      mini_batch_size: 50)
-
-    correct_answers = 0
-    test_data.data_pairs.each do |data_point|
-      result = cnn.run(data_point[:input], stealth: true)
-      if (result.index(result.max) == data_point[:output].index(data_point[:output].max))
-        correct_answers += 1
-      end
-    end
-    # cnn.inspect("activations")
-    puts "We managed #{correct_answers} out of #{test_data.data_pairs.size} total"
-    puts "Cnn output: #{cnn.output}"
-  end
+  # it "Figure out MNIST (mini-batch train, mse)" do
+  #   # Load training data (partial dataset)
+  #   raw_data = Array(Array(Float64)).new
+  #   csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_train.csv"))
+  #   1000.times do
+  #     # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
+  #     csv.next
+  #     new_row = Array(Float64).new
+  #     csv.row.to_a.each { |value| new_row << value.to_f64 }
+  #     raw_data << new_row
+  #   end
+  #   raw_input_data = Array(Array(Float64)).new
+  #   raw_output_data = Array(Array(Float64)).new
+
+  #   raw_data.each do |row|
+  #     raw_input_data << row[1..-1]
+  #     raw_output_data << [row[0]]
+  #   end
+
+  #   training_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)
+  #   training_data.for_mnist_conv
+  #   training_data.data_pairs.shuffle!
+
+  #   # puts "#{training_data.data_pairs.first[:output]}"
+  #   # training_data.data_pairs.first[:input].first.each do |row|
+  #   #   puts "#{row}"
+  #   # end
+
+  #   # Load test data (partial dataset)
+  #   raw_data = Array(Array(Float64)).new
+  #   csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_test.csv"))
+  #   100.times do
+  #     csv.next
+  #     new_row = Array(Float64).new
+  #     csv.row.to_a.each { |value| new_row << value.to_f64 }
+  #     raw_data << new_row
+  #   end
+
+  #   raw_input_data = Array(Array(Float64)).new
+  #   raw_output_data = Array(Array(Float64)).new
+
+  #   raw_data.each do |row|
+  #     raw_input_data << row[1..-1]
+  #     raw_output_data << [row[0]]
+  #   end
+
+  #   test_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)
+  #   test_data.for_mnist_conv
+
+  #   cnn = SHAInet::CNN.new
+  #   cnn.add_input([height = 28, width = 28, channels = 1]) # Output shape = 28x28x1
+  #   cnn.add_conv(
+  #     filters_num: 10,
+  #     window_size: 5,
+  #     stride: 1,
+  #     padding: 2,
+  #     activation_function: SHAInet.none) # Output shape = 28x28x20
+  #   cnn.add_relu(0.01)                   # Output shape = 28x28x20
+  #   cnn.add_fconnect(l_size: 10, activation_function: SHAInet.none)
+  #   cnn.add_softmax
+
+  #   cnn.learning_rate = 0.05
+  #   cnn.momentum = 0.02
+
+  #   # cnn.run(test_data.data_pairs.first[:input], stealth = false)
+  #   cnn.train_batch(
+  #     data: training_data.data_pairs,
+  #     training_type: :sgdm,
+  #     cost_function: :c_ent,
+  #     epochs: 10,
+  #     error_threshold: 0.0001,
+  #     log_each: 1,
+  #     mini_batch_size: 50)
+
+  #   correct_answers = 0
+  #   test_data.data_pairs.each do |data_point|
+  #     result = cnn.run(data_point[:input], stealth: true)
+  #     if (result.index(result.max) == data_point[:output].index(data_point[:output].max))
+  #       correct_answers += 1
+  #     end
+  #   end
+  #   # cnn.inspect("activations")
+  #   puts "We managed #{correct_answers} out of #{test_data.data_pairs.size} total"
+  #   puts "Cnn output: #{cnn.output}"
+  # end
 
   # it "Figure out MNIST (mini-batch train, cross-entropy)" do
   #   puts "Figure out MNIST (mini-batch train, cross-entropy)"
diff --git a/spec/network_spec.cr b/spec/network_spec.cr
index 96a68c0..2b9848f 100644
--- a/spec/network_spec.cr
+++ b/spec/network_spec.cr
@@ -5,521 +5,567 @@ require "csv"
 system("cd #{__DIR__}/test_data && tar xvf tests.tar.xz")
 
 describe SHAInet::Network do
-  # it "Initialize" do
-  #   nn = SHAInet::Network.new
-  #   nn.should be_a(SHAInet::Network)
-  # end
+  it "Initialize" do
+    nn = SHAInet::Network.new
+    nn.should be_a(SHAInet::Network)
+  end
 
-  # it "saves_to_file" do
-  #   nn = SHAInet::Network.new
-  #   nn.add_layer(:input, 2, :memory, SHAInet.sigmoid)
-  #   nn.add_layer(:output, 2, :memory, SHAInet.sigmoid)
-  #   nn.add_layer(:hidden, 2, :memory, SHAInet.sigmoid)
-  #   nn.fully_connect
-  #   nn.save_to_file("./my_net.nn")
-  #   File.exists?("./my_net.nn").should eq(true)
-  # end
+  it "saves_to_file" do
+    nn = SHAInet::Network.new
+    nn.add_layer(:input, 2, :memory, SHAInet.sigmoid)
+    nn.add_layer(:output, 2, :memory, SHAInet.sigmoid)
+    nn.add_layer(:hidden, 2, :memory, SHAInet.sigmoid)
+    nn.fully_connect
+    nn.save_to_file("./my_net.nn")
+    File.exists?("./my_net.nn").should eq(true)
+  end
 
-  # it "loads_from_file" do
-  #   nn = SHAInet::Network.new
-  #   nn.load_from_file("./my_net.nn")
-  #   (nn.all_neurons.size > 0).should eq(true)
-  # end
+  it "loads_from_file" do
+    nn = SHAInet::Network.new
+    nn.load_from_file("./my_net.nn")
+    (nn.all_neurons.size > 0).should eq(true)
+  end
 
-  # it "Test on a linear regression model" do
-  #   # data structures to hold the input and results
-  #   inputs = Array(Array(Float64)).new
-  #   outputs = Array(Array(Float64)).new
+  it "Test on a linear regression model" do
+    # data structures to hold the input and results
+    inputs = Array(Array(Float64)).new
+    outputs = Array(Array(Float64)).new
 
-  #   # read the file
-  #   raw = File.read("./spec/linear_data/data.csv")
-  #   csv = CSV.new(raw, headers: true)
+    # read the file
+    raw = File.read("./spec/linear_data/data.csv")
+    csv = CSV.new(raw, headers: true)
 
-  #   # load the data structures
-  #   while (csv.next)
-  #     inputs << [csv.row["Height"].to_f64]
-  #     outputs << [csv.row["Weight"].to_f64]
-  #   end
+    # load the data structures
+    while (csv.next)
+      inputs << [csv.row["Height"].to_f64]
+      outputs << [csv.row["Weight"].to_f64]
+    end
 
-  #   # normalize the data
-  #   training = SHAInet::TrainingData.new(inputs, outputs)
+    # normalize the data
+    training = SHAInet::TrainingData.new(inputs, outputs)
 
-  #   # create a network
-  #   model = SHAInet::Network.new
-  #   model.add_layer(:input, 1, :memory, SHAInet.none)
-  #   # model.add_layer(:hidden, 1, :memory, SHAInet.none)
-  #   model.add_layer(:output, 1, :memory, SHAInet.none)
-  #   model.fully_connect
+    # create a network
+    model = SHAInet::Network.new
+    model.add_layer(:input, 1, :memory, SHAInet.none)
+    # model.add_layer(:hidden, 1, :memory, SHAInet.none)
+    model.add_layer(:output, 1, :memory, SHAInet.none)
+    model.fully_connect
 
-  #   # Update learing rate (default is 0.005)
-  #   model.learning_rate = 0.01
+    # Update learing rate (default is 0.005)
+    model.learning_rate = 0.01
 
-  #   # train the network using Stochastic Gradient Descent with momentum
-  #   model.train(training.raw_data, :adam, :mse, 5000, 0.0, 1)
+    # train the network using Stochastic Gradient Descent with momentum
+    model.train(training.raw_data, :adam, :mse, 5000, 0.0, 1)
 
-  #   # model.show
+    # model.show
 
-  #   # Test model
-  #   output = model.run([1.47]).first
-  #   error = ((output - 51.008)/51.008).abs
-  #   (error < 0.05).should eq(true) # require less than 5% error
+    # Test model
+    output = model.run([1.47]).first
+    error = ((output - 51.008)/51.008).abs
+    (error < 0.05).should eq(true) # require less than 5% error
 
-  #   output = model.run([1.83]).first
-  #   error = ((output - 73.066)/73.066).abs
-  #   (error < 0.05).should eq(true) # require less than 5% error
-  # end
+    output = model.run([1.83]).first
+    error = ((output - 73.066)/73.066).abs
+    (error < 0.05).should eq(true) # require less than 5% error
+  end
 
-  # it "Figure out XOR with SGD + M" do
-  #   puts "---"
-  #   puts "Figure out XOR SGD + momentum (train, mse, sigmoid)"
-  #   training_data = [
-  #     [[0, 0], [0]],
-  #     [[1, 0], [1]],
-  #     [[0, 1], [1]],
-  #     [[1, 1], [0]],
-  #   ]
-
-  #   xor = SHAInet::Network.new
-
-  #   xor.add_layer(:input, 2, "memory", SHAInet.sigmoid)
-  #   1.times { |x| xor.add_layer(:hidden, 3, "memory", SHAInet.sigmoid) }
-  #   xor.add_layer(:output, 1, "memory", SHAInet.sigmoid)
-  #   xor.fully_connect
-
-  #   # xor.learning_rate = 0.7
-  #   # xor.momentum = 0.3
-
-  #   # xor.train(
-  #   #   data: training_data,
-  #   #   training_type: :sgdm,
-  #   #   cost_function: :mse,
-  #   #   epochs: 5000,
-  #   #   error_threshold: 0.000001,
-  #   #   log_each: 1000)
-
-  #   xor.train_es(
-  #     data: training_data,
-  #     pool_size: 100,
-  #     learning_rate: 0.2,
-  #     sigma: 0.3,
-  #     cost_function: :c_ent,
-  #     epochs: 1000,
-  #     mini_batch_size: 1,
-  #     error_threshold: 0.0,
-  #     log_each: 10)
-
-  #   (xor.run([0, 0]).first < 0.1).should eq(true)
-  #   (xor.run([1, 0]).first > 0.9).should eq(true)
-  #   (xor.run([0, 1]).first > 0.9).should eq(true)
-  #   (xor.run([1, 1]).first < 0.1).should eq(true)
-  # end
+  it "Figure out XOR with SGD + M" do
+    puts "---"
+    puts "Figure out XOR SGD + momentum (train, mse, sigmoid)"
+    training_data = [
+      [[0, 0], [0]],
+      [[1, 0], [1]],
+      [[0, 1], [1]],
+      [[1, 1], [0]],
+    ]
 
-  # it "Supports both Symbols or Strings as input params" do
-  #   puts "---"
-  #   puts "Supports both Symbols or Strings as input params (sgdm, train, mse, sigmoid)"
-  #   training_data = [
-  #     [[0, 0], [0]],
-  #     [[1, 0], [1]],
-  #     [[0, 1], [1]],
-  #     [[1, 1], [0]],
-  #   ]
-
-  #   xor = SHAInet::Network.new
-  #   xor.add_layer("input", 2, "memory", SHAInet.sigmoid)
-  #   1.times { |x| xor.add_layer("hidden", 3, "memory", SHAInet.sigmoid) }
-  #   xor.add_layer("output", 1, "memory", SHAInet.sigmoid)
-  #   xor.fully_connect
-
-  #   xor.learning_rate = 0.7
-  #   xor.momentum = 0.3
-
-  #   xor.train(
-  #     data: training_data,
-  #     training_type: "sgdm",
-  #     cost_function: "mse",
-  #     epochs: 5000,
-  #     error_threshold: 0.000001,
-  #     log_each: 1000)
-
-  #   (xor.run([0, 0]).first < 0.1).should eq(true)
-  #   (xor.run([1, 0]).first > 0.9).should eq(true)
-  #   (xor.run([0, 1]).first > 0.9).should eq(true)
-  #   (xor.run([1, 1]).first < 0.1).should eq(true)
-  # end
+    xor = SHAInet::Network.new
 
-  # it "works on iris dataset with batch train with Rprop (batch)" do
-  #   puts "---"
-  #   puts "works on iris dataset with Rprop (batch_train, mse, sigmoid)"
-  #   label = {
-  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-  #   }
-  #   iris = SHAInet::Network.new
-  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-  #   iris.fully_connect
-
-  #   iris.learning_rate = 0.7
-  #   iris.momentum = 0.3
-
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[0..-2].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     outputs << label[row[-1]]
-  #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
-
-  #   iris.train_batch(
-  #     data: normalized.data.shuffle,
-  #     training_type: :rprop,
-  #     cost_function: :mse,
-  #     epochs: 5000,
-  #     error_threshold: 0.000001,
-  #     log_each: 1000)
-
-  #   result = iris.run(normalized.normalized_inputs.first)
-  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.7)).should eq(true)
-  # end
+    xor.add_layer(:input, 2, "memory", SHAInet.sigmoid)
+    1.times { |x| xor.add_layer(:hidden, 3, "memory", SHAInet.sigmoid) }
+    xor.add_layer(:output, 1, "memory", SHAInet.sigmoid)
+    xor.fully_connect
 
-  # it "works on iris dataset with batch train with Adam (batch)" do
-  #   puts "---"
-  #   puts "works on iris dataset with Adam (batch_train, mse, sigmoid)"
-  #   label = {
-  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-  #   }
-  #   iris = SHAInet::Network.new
-  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-  #   iris.fully_connect
-
-  #   iris.learning_rate = 0.7
-  #   iris.momentum = 0.3
-
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[0..-2].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     outputs << label[row[-1]]
-  #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
-
-  #   iris.train_batch(
-  #     data: normalized.data.shuffle,
-  #     training_type: :adam,
-  #     cost_function: :mse,
-  #     epochs: 20000,
-  #     error_threshold: 0.00001,
-  #     log_each: 1000)
-
-  #   result = iris.run(normalized.normalized_inputs.first)
-  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
-  # end
+    xor.learning_rate = 0.7
+    xor.momentum = 0.3
 
-  # it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
-  #   puts "---"
-  #   puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
-  #   label = {
-  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-  #   }
-  #   iris = SHAInet::Network.new
-  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-  #   iris.fully_connect
-
-  #   iris.learning_rate = 0.7
-  #   iris.momentum = 0.3
-
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[0..-2].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     outputs << label[row[-1]]
-  #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
-
-  #   iris.train_batch(
-  #     data: normalized.data.shuffle,
-  #     training_type: :adam,
-  #     cost_function: :mse,
-  #     epochs: 5000,
-  #     error_threshold: 0.000001,
-  #     mini_batch_size: 50,
-  #     log_each: 1000)
-
-  #   result = iris.run(normalized.normalized_inputs.first)
-  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
-  # end
+    xor.train(
+      data: training_data,
+      training_type: :sgdm,
+      cost_function: :mse,
+      epochs: 5000,
+      error_threshold: 0.000001,
+      log_each: 1000)
+
+    (xor.run([0, 0]).first < 0.1).should eq(true)
+    (xor.run([1, 0]).first > 0.9).should eq(true)
+    (xor.run([0, 1]).first > 0.9).should eq(true)
+    (xor.run([1, 1]).first < 0.1).should eq(true)
+  end
 
-  # it "trains , saves, loads, runs" do
-  #   puts "---"
-  #   puts "train, save, loads and run works (Adam, mini-batch_train, mse, sigmoid)"
-  #   label = {
-  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-  #   }
-  #   iris = SHAInet::Network.new
-  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-  #   iris.fully_connect
-
-  #   iris.learning_rate = 0.7
-  #   iris.momentum = 0.3
-
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[0..-2].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     outputs << label[row[-1]]
-  #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
-
-  #   iris.train_batch(
-  #     data: normalized.data.shuffle,
-  #     training_type: :adam,
-  #     cost_function: :mse,
-  #     epochs: 5000,
-  #     error_threshold: 0.000001,
-  #     mini_batch_size: 50,
-  #     log_each: 1000)
-
-  #   iris.save_to_file("./my_net.nn")
-  #   nn = SHAInet::Network.new
-  #   nn.load_from_file("./my_net.nn")
-  #   result = nn.run(normalized.normalized_inputs.first)
-  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
-  # end
+  it "Supports both Symbols or Strings as input params" do
+    puts "---"
+    puts "Supports both Symbols or Strings as input params (sgdm, train, mse, sigmoid)"
+    training_data = [
+      [[0, 0], [0]],
+      [[1, 0], [1]],
+      [[0, 1], [1]],
+      [[1, 1], [0]],
+    ]
+
+    xor = SHAInet::Network.new
+    xor.add_layer("input", 2, "memory", SHAInet.sigmoid)
+    1.times { |x| xor.add_layer("hidden", 3, "memory", SHAInet.sigmoid) }
+    xor.add_layer("output", 1, "memory", SHAInet.sigmoid)
+    xor.fully_connect
+
+    xor.learning_rate = 0.7
+    xor.momentum = 0.3
+
+    xor.train(
+      data: training_data,
+      training_type: "sgdm",
+      cost_function: "mse",
+      epochs: 5000,
+      error_threshold: 0.000001,
+      log_each: 1000)
+
+    (xor.run([0, 0]).first < 0.1).should eq(true)
+    (xor.run([1, 0]).first > 0.9).should eq(true)
+    (xor.run([0, 1]).first > 0.9).should eq(true)
+    (xor.run([1, 1]).first < 0.1).should eq(true)
+  end
 
-  # it "Works with cross-entropy" do
-  #   puts "---"
-  #   puts "Works with cross-entropy (sgdm, mini-batch_train, cross-entropy, sigmoid + softmax)"
-  #   label = {
-  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-  #   }
-  #   iris = SHAInet::Network.new
-  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-  #   iris.fully_connect
-
-  #   iris.learning_rate = 0.7
-  #   iris.momentum = 0.3
-
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[0..-2].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     outputs << label[row[-1]]
-  #   end
-  #   normalized = SHAInet::TrainingData.new(inputs, outputs)
-  #   normalized.normalize_min_max
+  it "works on iris dataset with batch train with Rprop (batch)" do
+    puts "---"
+    puts "works on iris dataset with Rprop (batch_train, mse, sigmoid)"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    iris.learning_rate = 0.7
+    iris.momentum = 0.3
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
 
-  #   iris.train_batch(
-  #     data: normalized.data.shuffle,
-  #     training_type: :sgdm,
-  #     cost_function: :c_ent,
-  #     epochs: 100,
-  #     error_threshold: 0.000001,
-  #     mini_batch_size: 50,
-  #     log_each: 10)
-
-  #   result = iris.run(normalized.normalized_inputs.first)
-  #   expected = [0.0, 0.0, 1.0]
-  #   puts "result: \t#{result.map { |x| x.round(5) }}"
-  #   puts "expected: \t#{expected}"
-  #   ((result.first < 0.3) && (result[1] < 0.3) && (result.last > 0.9)).should eq(true)
-  # end
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
 
-  # it "works on iris dataset using evolutionary strategies as optimizer + cross-entropy" do
-  #   puts "---"
-  #   # puts "works on iris dataset using evolutionary strategies as optimizer + cross-entropy"
-  #   label = {
-  #     "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
-  #     "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
-  #     "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
-  #   }
-
-  #   iris = SHAInet::Network.new
-  #   iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
-  #   iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
-  #   iris.fully_connect
-
-  #   outputs = Array(Array(Float64)).new
-  #   inputs = Array(Array(Float64)).new
-  #   CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
-  #     row_arr = Array(Float64).new
-  #     row[0..-2].each do |num|
-  #       row_arr << num.to_f64
-  #     end
-  #     inputs << row_arr
-  #     outputs << label[row[-1]]
-  #   end
-  #   data = SHAInet::TrainingData.new(inputs, outputs)
-  #   data.normalize_min_max
+    training_data, test_data = data.split(0.9) # Split also shuffles
 
-  #   training_data, test_data = data.split(0.9)
+    iris.train_batch(
+      data: training_data,
+      training_type: :rprop,
+      cost_function: :mse,
+      epochs: 5000,
+      error_threshold: 0.000001,
+      log_each: 1000)
+
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
+    end
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+  end
 
-  #   iris.train_es(
-  #     data: training_data,
-  #     pool_size: 50,
-  #     learning_rate: 0.5,
-  #     sigma: 0.1,
-  #     cost_function: :c_ent,
-  #     epochs: 500,
-  #     mini_batch_size: 15,
-  #     error_threshold: 0.00000001,
-  #     log_each: 100,
-  #     show_slice: true)
+  it "works on iris dataset with batch train with Adam (batch)" do
+    puts "---"
+    puts "works on iris dataset with Adam (batch_train, mse, sigmoid)"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    iris.learning_rate = 0.7
+    iris.momentum = 0.3
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
 
-  #   # Test the trained model
-  #   correct = 0
-  #   test_data.data.each do |data_point|
-  #     result = iris.run(data_point[0], stealth: true)
-  #     expected = data_point[1]
-  #     # puts "result: \t#{result.map { |x| x.round(5) }}"
-  #     # puts "expected: \t#{expected}"
-  #     error_sum = 0.0
-  #     result.size.times do |i|
-  #       error_sum += (result[i] - expected[i]).abs
-  #     end
-  #     correct += 1 if error_sum < 0.3
-  #   end
-  #   puts "Correct answers: (#{correct} / #{test_data.size})"
-  #   (correct > 10).should eq(true)
-  # end
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
+
+    training_data, test_data = data.split(0.9) # Split also shuffles
+
+    iris.train_batch(
+      data: training_data,
+      training_type: :adam,
+      cost_function: :mse,
+      epochs: 20000,
+      error_threshold: 0.00001,
+      log_each: 1000)
+
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
+    end
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+  end
 
-  it "works on the mnist dataset using evolutionary optimizer and batch" do
-    mnist = SHAInet::Network.new
-    mnist.add_layer(:input, 784, "memory", SHAInet.sigmoid)
-    mnist.add_layer(:hidden, 50, "memory", SHAInet.sigmoid)
-    # mnist.add_layer(:hidden, 40, "eraser", SHAInet.sigmoid)
-    # mnist.add_layer(:hidden, 10, "memory", SHAInet.sigmoid)
-    # mnist.add_layer(:hidden, 100, "memory", SHAInet.sigmoid)
-    mnist.add_layer(:output, 10, "memory", SHAInet.sigmoid)
-
-    # Input to first hidden
-    mnist.fully_connect
-
-    # Load training data
-    raw_data = Array(Array(Float64)).new
-    csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_train.csv"))
-    10000.times do
-      # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
-      csv.next
-      new_row = Array(Float64).new
-      csv.row.to_a.each { |value| new_row << value.to_f64 }
-      raw_data << new_row
+  it "works on iris dataset with mini-batch train with Adam (mini-batch)" do
+    puts "---"
+    puts "works on iris dataset with Adam (mini-batch_train, mse, sigmoid)"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    iris.learning_rate = 0.7
+    iris.momentum = 0.3
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
     end
-    raw_input_data = Array(Array(Float64)).new
-    raw_output_data = Array(Array(Float64)).new
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
+
+    training_data, test_data = data.split(0.9) # Split also shuffles
+
+    iris.train_batch(
+      data: training_data,
+      training_type: :adam,
+      cost_function: :mse,
+      epochs: 5000,
+      error_threshold: 0.000001,
+      mini_batch_size: 10,
+      log_each: 1000)
+
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
+    end
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+  end
+
+  it "trains , saves, loads, runs" do
+    puts "---"
+    puts "train, save, loads and run works (Adam, mini-batch_train, mse, sigmoid)"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    iris.learning_rate = 0.7
+    iris.momentum = 0.3
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
+
+    training_data, test_data = data.split(0.9) # Split also shuffles
+
+    iris.train_batch(
+      data: training_data,
+      training_type: :adam,
+      cost_function: :mse,
+      epochs: 5000,
+      error_threshold: 0.000001,
+      mini_batch_size: 50,
+      log_each: 1000)
+
+    iris.save_to_file("./my_net.nn")
+    nn = SHAInet::Network.new
+    nn.load_from_file("./my_net.nn")
+
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
+    end
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+  end
+
+  it "Works with cross-entropy" do
+    puts "---"
+    puts "Works with cross-entropy (sgdm, mini-batch_train, cross-entropy)"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    iris.learning_rate = 0.7
+    iris.momentum = 0.3
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
+
+    training_data, test_data = data.split(0.9) # Split also shuffles
 
-    raw_data.each do |row|
-      raw_input_data << row[1..-1]
-      raw_output_data << [row[0]]
+    iris.train_batch(
+      data: training_data,
+      training_type: :sgdm,
+      cost_function: :c_ent,
+      epochs: 100,
+      error_threshold: 0.000001,
+      mini_batch_size: 50,
+      log_each: 10)
+
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
     end
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+  end
 
-    # Change digits to one-hot vectors
-    raw_output_data.each_with_index do |point, i|
-      lbl = point.first.clone.to_i
-      one_hot = Array(Float64).new(10) { 0.0 }
-      one_hot[lbl] = 1.0
-      raw_output_data[i] = one_hot
+  it "works on iris dataset using evolutionary strategies as optimizer + cross-entropy" do
+    puts "---"
+    # puts "works on iris dataset using evolutionary strategies as optimizer + cross-entropy"
+    label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
     end
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
 
-    training_data = SHAInet::TrainingData.new(raw_input_data, raw_output_data)
-    training_data.normalize_min_max
+    training_data, test_data = data.split(0.9)
 
-    # Train on the data
-    mnist.train_es(
+    iris.train_es(
       data: training_data,
       pool_size: 50,
       learning_rate: 0.5,
       sigma: 0.1,
       cost_function: :c_ent,
-      epochs: 10,
-      mini_batch_size: 100,
+      epochs: 500,
+      mini_batch_size: 15,
       error_threshold: 0.00000001,
-      log_each: 10,
+      log_each: 100,
       show_slice: true)
 
-    # Load test data
-    raw_data = Array(Array(Float64)).new
-    csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_test.csv"))
-    1000.times do
-      # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
-      csv.next
-      new_row = Array(Float64).new
-      csv.row.to_a.each { |value| new_row << value.to_f64 }
-      raw_data << new_row
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
     end
-    raw_input_data = Array(Array(Float64)).new
-    raw_output_data = Array(Array(Float64)).new
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+  end
 
-    # Change digits to one-hot vectors
-    raw_data.each do |row|
-      raw_input_data << row[1..-1]
-      raw_output_data << [row[0]]
-    end
+  # it "works on the mnist dataset using evolutionary optimizer and batch" do
+  #   mnist = SHAInet::Network.new
+  #   mnist.add_layer(:input, 784, "memory", SHAInet.sigmoid)
+  #   mnist.add_layer(:hidden, 50, "memory", SHAInet.sigmoid)
+  #   # mnist.add_layer(:hidden, 40, "eraser", SHAInet.sigmoid)
+  #   # mnist.add_layer(:hidden, 10, "memory", SHAInet.sigmoid)
+  #   # mnist.add_layer(:hidden, 100, "memory", SHAInet.sigmoid)
+  #   mnist.add_layer(:output, 10, "memory", SHAInet.sigmoid)
+
+  #   # Input to first hidden
+  #   mnist.fully_connect
+
+  #   # Load training data
+  #   raw_data = Array(Array(Float64)).new
+  #   csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_train.csv"))
+  #   10000.times do
+  #     # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
+  #     csv.next
+  #     new_row = Array(Float64).new
+  #     csv.row.to_a.each { |value| new_row << value.to_f64 }
+  #     raw_data << new_row
+  #   end
+  #   raw_input_data = Array(Array(Float64)).new
+  #   raw_output_data = Array(Array(Float64)).new
 
-    raw_output_data.each_with_index do |point, i|
-      lbl = point.first.clone.to_i
-      one_hot = Array(Float64).new(10) { 0.0 }
-      one_hot[lbl] = 1.0
-      raw_output_data[i] = one_hot
-    end
+  #   raw_data.each do |row|
+  #     raw_input_data << row[1..-1]
+  #     raw_output_data << [row[0]]
+  #   end
 
-    test_data = SHAInet::TrainingData.new(raw_input_data, raw_output_data)
-    test_data.normalize_min_max
-
-    # Run on all test data
-    results = Array(Int32).new
-    test_data.normalized_inputs.each_with_index do |test, i|
-      result = mnist.run(input: test, stealth: true)
-      if (result.index(result.max) == test_data.normalized_outputs[i].index(test_data.normalized_outputs[i].max))
-        results << 1
-      else
-        results << 0
-      end
-    end
-    puts "We managed #{results.sum} out of #{results.size} total"
-  end
+  #   training_data = SHAInet::TrainingData.new(raw_input_data, raw_output_data)
+  #   # training_data.normalize_min_max
+  #   training_data.normalized_inputs = training_data.normalize_min_max(data: training_data.inputs)
+  #   training_data.normalized_outputs = training_data.to_onehot(data: training_data.outputs, vector_size: 10)
+
+  #   # Train on the data
+  #   mnist.train_es(
+  #     data: training_data,
+  #     pool_size: 50,
+  #     learning_rate: 0.5,
+  #     sigma: 0.1,
+  #     cost_function: :c_ent,
+  #     epochs: 10,
+  #     mini_batch_size: 100,
+  #     error_threshold: 0.00000001,
+  #     log_each: 10,
+  #     show_slice: true)
+
+  #   # Load test data
+  #   raw_data = Array(Array(Float64)).new
+  #   csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_test.csv"))
+  #   1000.times do
+  #     # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|
+  #     csv.next
+  #     new_row = Array(Float64).new
+  #     csv.row.to_a.each { |value| new_row << value.to_f64 }
+  #     raw_data << new_row
+  #   end
+  #   raw_input_data = Array(Array(Float64)).new
+  #   raw_output_data = Array(Array(Float64)).new
+
+  #   test_data = SHAInet::TrainingData.new(raw_input_data, raw_output_data)
+  #   test_data.normalized_inputs = test_data.normalize_min_max(data: test_data.inputs)
+  #   test_data.normalized_outputs = test_data.to_onehot(data: test_data.outputs, vector_size: 10)
+
+  #   # Run on all test data
+  #   results = Array(Int32).new
+  #   test_data.normalized_inputs.each_with_index do |test, i|
+  #     result = mnist.run(input: test, stealth: true)
+  #     if (result.index(result.max) == test_data.normalized_outputs[i].index(test_data.normalized_outputs[i].max))
+  #       results << 1
+  #     else
+  #       results << 0
+  #     end
+  #   end
+  #   puts "We managed #{results.sum} out of #{results.size} total"
+  # end
 end
 
 # Remove train data
diff --git a/src/shainet/basic/data.cr b/src/shainet/basic/data.cr
index 14381c8..f242f28 100644
--- a/src/shainet/basic/data.cr
+++ b/src/shainet/basic/data.cr
@@ -6,9 +6,8 @@ module SHAInet
     @ymax : Int32
     @ymin : Int32
 
-    getter :normalized_outputs, :normalized_inputs, :labels
-    getter :inputs, :outputs
-    setter :outputs
+    property :normalized_outputs, :normalized_inputs, :labels, :outputs
+    getter :inputs
 
     # @data_pairs :
     # Takes a path to a CSV file, a range of inputs and the index of the target column.
@@ -68,6 +67,19 @@ module SHAInet
       return arr
     end
 
+    def normalize_min_max(data : Array(Array(Float64)))
+      normalized_data = Array(Array(Float64)).new
+
+      # Get min-max
+      data.transpose.each { |a| @i_max << a.max; @i_min << a.min }
+
+      data.each do |row|
+        normalized_data << normalize_inputs(row)
+      end
+
+      return normalized_data
+    end
+
     def normalize_min_max
       # Get inputs min-max
       @inputs.transpose.each { |a| @i_max << a.max; @i_min << a.min }
@@ -173,5 +185,16 @@ module SHAInet
     def size
       return @inputs.size
     end
+
+    def to_onehot(data : Array(Array(Float64)), vector_size : Int32)
+      data.each_with_index do |point, i|
+        lbl = point.first.clone.to_i
+        one_hot = Array(Float64).new(vector_size) { 0.0 }
+        one_hot[lbl] = 1.0
+        data[i] = one_hot
+      end
+
+      return data
+    end
   end
 end
diff --git a/src/shainet/basic/es.cr b/src/shainet/basic/es.cr
index 5b52a9a..501d99b 100644
--- a/src/shainet/basic/es.cr
+++ b/src/shainet/basic/es.cr
@@ -107,9 +107,6 @@ module SHAInet
                    @sigma : Float64,
                    original_biases : Array(Float64),
                    original_weights : Array(Float64))
-      #
-      # @learning_rate = rand(0.0..1.0)
-      # @sigma = rand(0.0..1.0)
       @mse = 0.0
       @reward = 0.0
       @error_signal = [] of Float64
@@ -123,25 +120,13 @@ module SHAInet
     def get_new_params
       # Update biases
       @network.all_neurons.each_with_index do |neuron, i|
-        # threshold = @sigma*(neuron.bias.clone).abs
-        # change = rand(-threshold..threshold) # Add noise
-
-        # change = rand(-@sigma..@sigma) # Add noise
-        # new_value = @original_biases[i].clone + change*@learning_rate
-
         new_value = SHAInet::RandomNormal.sample(n: 1, mu: neuron.bias, sigma: @sigma).first
-        # puts "new_value: #{new_value}"
         neuron.bias = new_value.clone
         @biases[i] = new_value.clone
       end
 
       # Update weights
       @network.all_synapses.each_with_index do |synapse, i|
-        # threshold = @sigma*(synapse.weight.clone).abs
-        # change = rand(-threshold..threshold) # Add noise
-        # # change = rand(-@sigma..@sigma) # Add noise
-        # new_value = @original_weights[i].clone + change*@learning_rate
-
         new_value = SHAInet::RandomNormal.sample(n: 1, mu: synapse.weight, sigma: @sigma).first
         synapse.weight = new_value
         @weights[i] = new_value