RNN

docs/user/tutorial.rst

@@ -84,17 +84,17 @@ Layers names are optional.
     # Dropout Layer 1
     l_dro1 = yadll.layers.Dropout(incoming=l_in, corruption_level=0.4, name='Dropout 1')
     # Dense Layer 1
-    l_hid1 = yadll.layers.DenseLayer(incoming=l_dro1, nb_units=500, W=yadll.init.glorot_uniform,
+    l_hid1 = yadll.layers.DenseLayer(incoming=l_dro1, n_units=500, W=yadll.init.glorot_uniform,
                                      l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
                                      name='Hidden layer 1')
     # Dropout Layer 2
     l_dro2 = yadll.layers.Dropout(incoming=l_hid1, corruption_level=0.2, name='Dropout 2')
     # Dense Layer 2
-    l_hid2 = yadll.layers.DenseLayer(incoming=l_dro2, nb_units=500, W=yadll.init.glorot_uniform,
+    l_hid2 = yadll.layers.DenseLayer(incoming=l_dro2, n_units=500, W=yadll.init.glorot_uniform,
                                      l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
                                      name='Hidden layer 2')
     # Logistic regression Layer
-    l_out = yadll.layers.LogisticRegression(incoming=l_hid2, nb_class=10, l1=hp.l1_reg,
+    l_out = yadll.layers.LogisticRegression(incoming=l_hid2, n_class=10, l1=hp.l1_reg,
                                             l2=hp.l2_reg, name='Logistic regression')
 
 We create a :class:`yadll.network.Network` object and add all the layers sequentially.
@@ -246,17 +246,17 @@ When loading the parameters, the network name must match the saved parameters ne
     # Dropout Layer 1
     l_dro1 = yadll.layers.Dropout(incoming=l_in, corruption_level=0.4, name='Dropout 1')
     # Dense Layer 1
-    l_hid1 = yadll.layers.DenseLayer(incoming=l_dro1, nb_units=500, W=yadll.init.glorot_uniform,
+    l_hid1 = yadll.layers.DenseLayer(incoming=l_dro1, n_units=500, W=yadll.init.glorot_uniform,
                                      l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
                                      name='Hidden layer 1')
     # Dropout Layer 2
     l_dro2 = yadll.layers.Dropout(incoming=l_hid1, corruption_level=0.2, name='Dropout 2')
     # Dense Layer 2
-    l_hid2 = yadll.layers.DenseLayer(incoming=l_dro2, nb_units=500, W=yadll.init.glorot_uniform,
+    l_hid2 = yadll.layers.DenseLayer(incoming=l_dro2, n_units=500, W=yadll.init.glorot_uniform,
                                      l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
                                      name='Hidden layer 2')
     # Logistic regression Layer
-    l_out = yadll.layers.LogisticRegression(incoming=l_hid2, nb_class=10, l1=hp.l1_reg,
+    l_out = yadll.layers.LogisticRegression(incoming=l_hid2, n_class=10, l1=hp.l1_reg,
                                             l2=hp.l2_reg, name='Logistic regression')
 
     # Create network and add layers
@@ -382,13 +382,13 @@ Now we will loop over each possible combination
         # Input layer
         l_in = InputLayer(shape=(None, 28 * 28), name='Input')
         # Dense Layer 1
-        l_hid1 = DenseLayer(incoming=l_in, nb_units=5, W=hp.initialisation, l1=hp.l1_reg,
+        l_hid1 = DenseLayer(incoming=l_in, n_units=5, W=hp.initialisation, l1=hp.l1_reg,
                             l2=hp.l2_reg, activation=hp.activation, name='Hidden layer 1')
         # Dense Layer 2
-        l_hid2 = DenseLayer(incoming=l_hid1, nb_units=5, W=hp.initialisation, l1=hp.l1_reg,
+        l_hid2 = DenseLayer(incoming=l_hid1, n_units=5, W=hp.initialisation, l1=hp.l1_reg,
                             l2=hp.l2_reg, activation=hp.activation, name='Hidden layer 2')
         # Logistic regression Layer
-        l_out = LogisticRegression(incoming=l_hid2, nb_class=10, l1=hp.l1_reg,
+        l_out = LogisticRegression(incoming=l_hid2, n_class=10, l1=hp.l1_reg,
                                    l2=hp.l2_reg, name='Logistic regression')
 
         # Create network and add layers

examples/hp_grid_search_example.py

@@ -51,13 +51,13 @@ def grid_search():
         # Input layer
         l_in = InputLayer(input_shape=(None, 28 * 28), name='Input')
         # Dense Layer 1
-        l_hid1 = DenseLayer(incoming=l_in, nb_units=5, W=hp.initialisation, l1=hp.l1_reg,
+        l_hid1 = DenseLayer(incoming=l_in, n_units=5, W=hp.initialisation, l1=hp.l1_reg,
                             l2=hp.l2_reg, activation=hp.activation, name='Hidden layer 1')
         # Dense Layer 2
-        l_hid2 = DenseLayer(incoming=l_hid1, nb_units=5, W=hp.initialisation, l1=hp.l1_reg,
+        l_hid2 = DenseLayer(incoming=l_hid1, n_units=5, W=hp.initialisation, l1=hp.l1_reg,
                             l2=hp.l2_reg, activation=hp.activation, name='Hidden layer 2')
         # Logistic regression Layer
-        l_out = LogisticRegression(incoming=l_hid2, nb_class=10, l1=hp.l1_reg,
+        l_out = LogisticRegression(incoming=l_hid2, n_class=10, l1=hp.l1_reg,
                                    l2=hp.l2_reg, name='Logistic regression')
 
         # Create network and add layers

examples/lstm_example.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+"""
+This example show you how to train an LSTM for text generation.
+"""
+import os
+import yadll
+import logging
+
+logging.basicConfig(level=logging.DEBUG, format='%(message)s')
+
+# load the data
+datafile = 'nietzsche.txt'
+if not os.path.isfile(datafile):
+    import urllib
+    origin = 'https://s3.amazonaws.com/text-datasets/nietzsche.txt'
+    print 'Downloading data from %s' % origin
+    urllib.urlretrieve(origin, datafile)
+data = yadll.data.Data(datafile)
+
+# create the model
+model = yadll.model.Model(name='lstm', data=data)
+
+# Hyperparameters
+hp = yadll.hyperparameters.Hyperparameters()
+hp('batch_size', 128)
+hp('n_epochs', 1000)
+hp('learning_rate', 0.9)
+hp('momentum', 0.5)
+hp('l1_reg', 0.00)
+hp('l2_reg', 0.0000)
+hp('patience', 10000)
+
+# add the hyperparameters to the model
+model.hp = hp
+
+# Create connected layers
+# Input layer
+l_in = yadll.layers.InputLayer(input_shape=(hp.batch_size, 28 * 28), name='Input')
+# Dropout Layer 1
+l_dro1 = yadll.layers.Dropout(incoming=l_in, corruption_level=0.4, name='Dropout 1')
+# Dense Layer 1
+l_hid1 = yadll.layers.DenseLayer(incoming=l_dro1, n_units=100, W=yadll.init.glorot_uniform,
+                                 l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
+                                 name='Hidden layer 1')
+# Dropout Layer 2
+l_dro2 = yadll.layers.Dropout(incoming=l_hid1, corruption_level=0.2, name='Dropout 2')
+# Dense Layer 2
+l_hid2 = yadll.layers.DenseLayer(incoming=l_dro2, n_units=100, W=yadll.init.glorot_uniform,
+                                 l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
+                                 name='Hidden layer 2')
+# Logistic regression Layer
+l_out = yadll.layers.LogisticRegression(incoming=l_hid2, n_class=10, l1=hp.l1_reg,
+                                        l2=hp.l2_reg, name='Logistic regression')
+
+# Create network and add layers
+net = yadll.network.Network('2 layers mlp with dropout')
+net.add(l_in)
+net.add(l_dro1)
+net.add(l_hid1)
+net.add(l_dro2)
+net.add(l_hid2)
+net.add(l_out)
+
+# add the network to the model
+model.network = net
+
+# updates method
+model.updates = yadll.updates.newton
+
+# train the model and save it to file at each best
+model.train()
+
+# saving network paramters
+net.save_params('net_params.yp')
+
+# make prediction
+# We can test it on some examples from test
+test_set_x = data.test_set_x.get_value()
+test_set_y = data.test_set_y.eval()
+
+predicted_values = model.predict(test_set_x[:30])
+
+print ("Model 1, predicted values for the first 30 examples in test set:")
+print predicted_values
+print test_set_y[:30]

examples/model_template.py

@@ -40,17 +40,17 @@
 # Dropout Layer 1
 l_dro1 = yadll.layers.Dropout(incoming=l_in, corruption_level=0.4, name='Dropout 1')
 # Dense Layer 1
-l_hid1 = yadll.layers.DenseLayer(incoming=l_dro1, nb_units=100, W=yadll.init.glorot_uniform,
+l_hid1 = yadll.layers.DenseLayer(incoming=l_dro1, n_units=100, W=yadll.init.glorot_uniform,
                                  l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
                                  name='Hidden layer 1')
 # Dropout Layer 2
 l_dro2 = yadll.layers.Dropout(incoming=l_hid1, corruption_level=0.2, name='Dropout 2')
 # Dense Layer 2
-l_hid2 = yadll.layers.DenseLayer(incoming=l_dro2, nb_units=100, W=yadll.init.glorot_uniform,
+l_hid2 = yadll.layers.DenseLayer(incoming=l_dro2, n_units=100, W=yadll.init.glorot_uniform,
                                  l1=hp.l1_reg, l2=hp.l2_reg, activation=yadll.activations.relu,
                                  name='Hidden layer 2')
 # Logistic regression Layer
-l_out = yadll.layers.LogisticRegression(incoming=l_hid2, nb_class=10, l1=hp.l1_reg,
+l_out = yadll.layers.LogisticRegression(incoming=l_hid2, n_class=10, l1=hp.l1_reg,
                                         l2=hp.l2_reg, name='Logistic regression')
 
 # Create network and add layers