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import numpy as np
from scipy.special import expit
import sys
import os
from optimizers import *
def print_shape(nparry):
class NeuralNetwork(object):
""" Feedforward neural network with a single hidden layer
n_output: int: number of output units, equal to num class labels
n_features: int: number of features in the input dataset
n_hidden: int: (default 30): num hidden units
l2: float(default: 0.0) - lambda value for L2 regularization
epochs: int (default = 500) - passes over training set
learning_rate: float (default: 0.001) - learning reate
momentum_const: float (default: 0.0) - momentum constant - multiplied with gradient of previous pass through set
decay_rate: float (default 0.0) - shrinks learning rate after each epoch
minibatch_size: int (default: 1) - divides training data into batches for efficiency
def __init__(self, n_output, n_features, n_hidden=30, l2=0.0, epochs=500,
learning_rate=0.001, momentum_const=0.0, decay_rate=0.0,
dropout=False, minibatch_size=1,
optimizer = 'Gradient Descent', activation = 'relu',
nesterov = False, check_gradients = False, early_stop = None, metrics = ['Accuracy']):
self.n_output = n_output
self.n_features = n_features
self.n_hidden = n_hidden
self.w1, self.w2 = self.initialize_weights()
self.l2 = l2
self.epochs = epochs
self.learning_rate = learning_rate
self.momentum_const = momentum_const
self.decay_rate = decay_rate
self.dropout = dropout
self.minibatch_size = minibatch_size
self.nesterov = nesterov
self.check_gradients = check_gradients
supported_optimizers = ['Gradient Descent', 'Momentum', 'Nesterov', 'Adam', 'Adagrad', 'Adadelta', 'RMSProp']
if optimizer not in supported_optimizers:
print("Error: unsupported optimizer requested.")
print("Available optimizers: {}".format(supported_optimizers))
self.optimizer = optimizer
supported_activations = ['relu', 'tanh', 'sigmoid', 'maxout', 'elu']
if activation not in supported_activations:
print("Error: unsupported activation requested.")
print("Available activations: {}".format(supported_activations))
self.activation = activation
self.early_stop = early_stop
SUPPORTED_METRICS = ['Accuracy', 'Precision', 'Recall', 'AUC']
for elem in metrics:
assert elem in SUPPORTED_METRICS
self.metrics = metrics
def initialize_weights(self):
init weights with random nums uniformly with small values, dividing by fanin for xavier
w1 = np.random.uniform(-1.0, 1.0, size = self.n_hidden * (self.n_features + 1)).reshape(self.n_hidden, (self.n_features + 1))/(self.n_features + 1)
w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden+1)).reshape(self.n_output, self.n_hidden+ 1)/(self.n_hidden + 1)
return w1, w2
def encode_labels(self, y, num_labels):
""" Encode labels into a one-hot representation
y: array of num_samples, contains the target class labels for each training example.
For example, y = [2, 1, 3, 3] -> 4 training samples, and the ith sample has label y[i]
k: number of output labels
returns: onehot, a matrix of labels by samples. For each column, the ith index will be
"hot", or 1, to represent that index being the label.
onehot = np.zeros((num_labels, y.shape[0]))
for i in range(y.shape[0]):
onehot[y[i], i] = 1.0
return onehot
def softmax(self, v):
"""Calculates the softmax function that outputs a vector of values that sum to one.
We take max(softmax(v)) to be the predicted label. The output of the softmax function
is also used to calculate the cross-entropy loss
logC = -np.max(v)
return np.exp(v + logC)/np.sum(np.exp(v + logC), axis = 0)
def tanh(self, z, deriv=False):
""" Compute the tanh function or its derivative.
return np.tanh(z) if not deriv else 1 - np.square(np.tanh(z))
def relu(self, z, deriv = False):
if not deriv:
relud = z
relud[relud < 0] = 0
return relud
deriv = z
deriv[deriv <= 0] = 0
deriv[deriv > 0] = 1
return deriv
def add_bias_unit(self, X, column=True):
"""Adds a bias unit to our inputs"""
if column:
bias_added = np.ones((X.shape[0], X.shape[1] + 1))
bias_added[:, 1:] = X
bias_added = np.ones((X.shape[0] + 1, X.shape[1]))
bias_added[1:, :] = X
return bias_added
def compute_dropout(self, activations, dropout_prob = 0.5):
"""Sets half of the activations to zero
Params: activations - numpy array
Return: activations, which half set to zero
# handle error
if dropout_prob < 0 or dropout_prob > 1:
dropout_prob = 0.5
# scale the activations (see
mult = np.random.binomial(1, 0.5, size = activations.shape)
return activations
def forward(self, X, w1, w2, do_dropout = True):
""" Compute feedforward step
X: matrix of num_samples by num_features, input layer with samples and features
w1: matrix of weights from input layer to hidden layer. Dimensionality of num_hidden_units by num_features + 1 (bias)
w2: matrix of weights from hidden layer to output layer. Dimensionality of num_output_units (equal to num class labels) by num_hidden units + 1 (bias)
dropout: If true, randomly set half of the activations to zero to prevent overfitting.
#the activation of the input layer is simply the input matrix plus bias unit, added for each sample.
a1 = self.add_bias_unit(X)
if self.dropout and do_dropout: a1 = self.compute_dropout(a1)
#the input of the hidden layer is obtained by applying our weights to our inputs. We essentially take a linear combination of our inputs
z2 =
#applies the tanh function to obtain the input mapped to a distrubution of values between -1 and 1
a2 = self.tanh(z2)
#add a bias unit to activation of the hidden layer.
a2 = self.add_bias_unit(a2, column=False)
if self.dropout and do_dropout: a2 = self.compute_dropout(a2)
# compute input of output layer in exactly the same manner.
z3 =
# the activation of our output layer is just the softmax function.
a3 = self.softmax(z3)
return a1, z2, a2, z3, a3
def get_cost(self, y_enc, output, w1, w2):
""" Compute the cost function.
y_enc: array of num_labels x num_samples. class labels one-hot encoded
output: matrix of output_units x samples - activation of output layer from feedforward
w1: weight matrix of input to hidden layer
w2: weight matrix of hidden to output layer
cost = - np.sum(y_enc*np.log(output))
# add the L2 regularization by taking the L2-norm of the weights and multiplying it with our constant.
l2_term = (self.l2/2.0) * (np.sum(np.square(w1[:, 1:])) + np.sum(np.square(w2[:, 1:])))
cost = cost + l2_term
return cost/y_enc.shape[1]
def backprop(self, a1, a2, a3, z2, y_enc, w1, w2):
""" Computes the gradient using backpropagation
a1: array of n_samples by features+1 - activation of input layer (just input plus bias)
a2: activation of hidden layer
a3: activation of output layer
z2: input of hidden layer
y_enc: onehot encoded class labels
w1: weight matrix of input layer to hidden layer
w2: weight matrix of hidden to output layer
returns: grad1, grad2: gradient of weight matrix w1, gradient of weight matrix w2
#backpropagate our error
sigma3 = a3 - y_enc
z2 = self.add_bias_unit(z2, column=False)
sigma2 = * self.tanh(z2, deriv=True)
#get rid of the bias row
sigma2 = sigma2[1:, :]
grad1 =
grad2 =
# add the regularization term
grad1[:, 1:]+= (w1[:, 1:]*self.l2) # derivative of .5*l2*w1^2
grad2[:, 1:]+= (w2[:, 1:]*self.l2) # derivative of .5*l2*w2^2
return grad1, grad2
def accuracy(self, X_train, y_train):
"""Calculate the training accuracy. Requires passing through the entire dataset."""
y_train_pred = self.predict(X_train)
diffs = y_train_pred - y_train
count = 0.
for i in range(y_train.shape[0]):
if diffs[i] != 0:
return 100 - count*100/y_train.shape[0]
def predict(self, X, dropout = False):
"""Generate a set of predicted labels for the input dataset"""
a1, z2, a2, z3, a3 = self.forward(X, self.w1, self.w2, do_dropout = False)
#z3 is of dimension output units x num_samples. each row is an array representing the likelihood that the sample belongs to the class label given by the index...
#ex: first row of z3 = [0.98, 0.78, 0.36]. This means our network has 3 output units = 3 class labels. And this instance most likely belongs to the class given by the label 0.
y_pred = np.argmax(a3, axis = 0)
return y_pred
def fit(self, X, y, print_progress=True):
""" Learn weights from training data
X: matrix of samples x features. Input layer
y: target class labels of the training instances (ex: y = [1, 3, 4, 4, 3])
print_progress: True if you want to see the loss and training accuracy, but it is expensive.
X_data, y_data = X.copy(), y.copy()
y_enc = self.encode_labels(y, self.n_output)
prev_grad_w1 = np.zeros(self.w1.shape)
prev_grad_w2 = np.zeros(self.w2.shape)
costs = []
grad_1_li, grad_2_li = [], [] # used to keep list of gradients which can be used to measure and differentiate between learning speed of input -> hidden and hidden -> output layer weights
#pass through the dataset
for i in range(self.epochs):
previous_accuracies = []
self.learning_rate /= (1 + self.decay_rate*i)
mini = np.array_split(range(y_data.shape[0]), self.minibatch_size)
grads_w1, grads_w2 = [], [] # needed if we want to remember averages of gradients across time
for idx in mini:
#feed feedforward
a1, z2, a2, z3, a3= self.forward(X_data[idx], self.w1, self.w2)
cost = self.get_cost(y_enc=y_enc[:, idx], output=a3, w1=self.w1, w2=self.w2)
#compute gradient via backpropagation
grad1, grad2 = self.backprop(a1=a1, a2=a2, a3=a3, z2=z2, y_enc=y_enc[:, idx], w1=self.w1, w2=self.w2)
if self.check_gradients:
eps = 10e-4
# compute numerical gradient
w1_check, w2_check = self.w1, self.w2
g1_check, g2_check = grad1, grad2
w1_check = w1_check.reshape((w1_check.shape[0] * w1_check.shape[1]))
w2_check = w2_check.reshape((w2_check.shape[0] * w2_check.shape[1]))
g1_check = g1_check.reshape((g1_check.shape[0] * g1_check.shape[1]))
g2_check = g2_check.reshape((g2_check.shape[0] * g2_check.shape[1]))
for check in [w1_check, w2_check, g1_check, g2_check]:
for i in range(w1_check.shape[0]):
E = np.zeros(w1_check.shape[0])
E[i] = 1
w1_check_2 = w1_check + (eps * E)
w1_check_3 = w1_check - (eps * E)
for j in range(w1_check.shape[0]):
if j != i:
assert w1_check_2[j] == w1_check[j], "Houston we have a problem"
assert w1_check_3[j] == w1_check[j], "Houston we have a problem"
J1 = self.get_cost(y_enc=y_enc[:, idx], output=a3, w1=w1_check_2.reshape((self.w1.shape[0], self.w1.shape[1])), w2=self.w2)
J2 = self.get_cost(y_enc=y_enc[:, idx], output=a3, w1=w1_check_3.reshape((self.w1.shape[0], self.w1.shape[1])), w2=self.w2)
numerical_gradient = (J1 - J2) / (2 * eps)
if i % 100 == 0:
print("numerical gradient is {} and actual gradient is {}".format(numerical_gradient, g1_check[i]))
# update parameters, multiplying by learning rate + momentum constants
# w1_update, w2_update = self.momentum_optimizer(self.learning_rate, grad1, grad2)
w1_update, w2_update = self.learning_rate*grad1, self.learning_rate*grad2
# OPTIMIZERS TEST - remove this later
# w1_update, w2_update = vanilla_gd([self.learning_rate], grad1, grad2)
# self.w1 += -(w1_update)
# self.w2 += -(w2_update)
# continue
if self.nesterov:
# v_prev = v # back this up
# v = mu * v - learning_rate * dx # velocity update stays the same
# x += -mu * v_prev + (1 + mu) * v # position update changes form
# psuedocode from
v1 = self.momentum_const * prev_grad_w1 - w1_update
v2 = self.momentum_const * prev_grad_w2 - w2_update
self.w1 += -self.momentum_const * prev_grad_w1 + (1 + self.momentum_const) * v1
self.w2 += -self.momentum_const * prev_grad_w2 + (1 + self.momentum_const) * v2
# gradient update: w += -alpha * gradient.
# use momentum - add in previous gradient mutliplied by a momentum hyperparameter.
self.w1 += -(w1_update + (self.momentum_const*prev_grad_w1))
self.w2 += -(w2_update + (self.momentum_const*prev_grad_w2))
# save previous gradients for momentum
# grads_w1.append(w1_update)
# grads_w2.append(w2_update)
# prev_grad_w1 = np.mean(grads_w1)
# prev_grad_w2 = np.mean(grads_w2)
# alternate way that just remembers the previous gradient
prev_grad_w1, prev_grad_w2 = w1_update, w2_update
if print_progress and (i+1) % 1 == 0:
print("Epoch: {}".format(i + 1))
print("Loss: {}".format(cost))
if self.check_gradients:
print("Gradient Error: {}".format(w1_grad_error))
grad_1_mag, grad_2_mag = np.linalg.norm(grad_1_li), np.linalg.norm(grad_2_li)
acc = self.accuracy(X, y)
if self.early_stop is not None and len(previous_accuracies) > 3:
if abs(previous_accuracies[-1] - previous_accuracies[-2]) < self.early_stop and abs(previous_accuracies[-1] - previous_accuracies[-3]) < self.early_stop:
print("Early stopping, accuracy has stayed roughly constant over last 100 iterations.")
print("Training Accuracy: {}".format(acc))
return self