aima-python/deep_learning4e.py at master · DennisFX/aima-python · GitHub

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"""Deep learning. (Chapters 20)"""

import random

import statistics

import numpy as np

from keras import Sequential, optimizers

from keras.layers import Embedding, SimpleRNN, Dense

from keras.preprocessing import sequence

from utils4e import (conv1D, gaussian_kernel, element_wise_product, vector_add, random_weights,

scalar_vector_product, map_vector, mean_squared_error_loss)

class Node:

"""

A single unit of a layer in a neural network

:param weights: weights between parent nodes and current node

:param value: value of current node

"""

def __init__(self, weights=None, value=None):

self.value = value

self.weights = weights or []

class Layer:

"""

A layer in a neural network based on a computational graph.

:param size: number of units in the current layer

"""

def __init__(self, size):

self.nodes = np.array([Node() for _ in range(size)])

def forward(self, inputs):

"""Define the operation to get the output of this layer"""

raise NotImplementedError

class Activation:

def function(self, x):

return NotImplementedError

def derivative(self, x):

return NotImplementedError

def __call__(self, x):

return self.function(x)

class Sigmoid(Activation):

def function(self, x):

return 1 / (1 + np.exp(-x))

def derivative(self, value):

return value * (1 - value)

class ReLU(Activation):

def function(self, x):

return max(0, x)

def derivative(self, value):

return 1 if value > 0 else 0

class ELU(Activation):

def __init__(self, alpha=0.01):

self.alpha = alpha

def function(self, x):

return x if x > 0 else self.alpha * (np.exp(x) - 1)

def derivative(self, value):

return 1 if value > 0 else self.alpha * np.exp(value)

class LeakyReLU(Activation):

def __init__(self, alpha=0.01):

self.alpha = alpha

def function(self, x):

return max(x, self.alpha * x)

def derivative(self, value):

return 1 if value > 0 else self.alpha

class Tanh(Activation):

def function(self, x):

return np.tanh(x)

def derivative(self, value):

return 1 - (value ** 2)

class SoftMax(Activation):

def function(self, x):

return np.exp(x) / np.sum(np.exp(x))

def derivative(self, x):

return np.ones_like(x)

class SoftPlus(Activation):

def function(self, x):

return np.log(1. + np.exp(x))

def derivative(self, x):

return 1. / (1. + np.exp(-x))

class Linear(Activation):

def function(self, x):

return x

def derivative(self, x):

return np.ones_like(x)

class InputLayer(Layer):

"""1D input layer. Layer size is the same as input vector size."""

def __init__(self, size=3):

super().__init__(size)

def forward(self, inputs):

"""Take each value of the inputs to each unit in the layer."""

assert len(self.nodes) == len(inputs)

for node, inp in zip(self.nodes, inputs):

node.value = inp

return inputs

class OutputLayer(Layer):

"""1D softmax output layer in 19.3.2."""

def __init__(self, size=3):

super().__init__(size)

def forward(self, inputs, activation=SoftMax):

assert len(self.nodes) == len(inputs)

res = activation().function(inputs)

for node, val in zip(self.nodes, res):

node.value = val

return res

class DenseLayer(Layer):

"""

1D dense layer in a neural network.

:param in_size: (int) input vector size

:param out_size: (int) output vector size

:param activation: (Activation object) activation function

"""

def __init__(self, in_size=3, out_size=3, activation=Sigmoid):

super().__init__(out_size)

self.out_size = out_size

self.inputs = None

self.activation = activation()

# initialize weights

for node in self.nodes:

node.weights = random_weights(-0.5, 0.5, in_size)

def forward(self, inputs):

self.inputs = inputs

res = []

# get the output value of each unit

for unit in self.nodes:

val = self.activation.function(np.dot(unit.weights, inputs))

unit.value = val

res.append(val)

return res

class ConvLayer1D(Layer):

"""

1D convolution layer of in neural network.

:param kernel_size: convolution kernel size

"""

def __init__(self, size=3, kernel_size=3):

super().__init__(size)

# init convolution kernel as gaussian kernel

for node in self.nodes:

node.weights = gaussian_kernel(kernel_size)

def forward(self, features):

# each node in layer takes a channel in the features

assert len(self.nodes) == len(features)

res = []

# compute the convolution output of each channel, store it in node.val

for node, feature in zip(self.nodes, features):

out = conv1D(feature, node.weights)

res.append(out)

node.value = out

return res

class MaxPoolingLayer1D(Layer):

"""

1D max pooling layer in a neural network.

:param kernel_size: max pooling area size

"""

def __init__(self, size=3, kernel_size=3):

super().__init__(size)

self.kernel_size = kernel_size

self.inputs = None

def forward(self, features):

assert len(self.nodes) == len(features)

res = []

self.inputs = features

# do max pooling for each channel in features

for i in range(len(self.nodes)):

feature = features[i]

# get the max value in a kernel_size * kernel_size area

out = [max(feature[i:i + self.kernel_size])

for i in range(len(feature) - self.kernel_size + 1)]

res.append(out)

self.nodes[i].value = out

return res

class BatchNormalizationLayer(Layer):

"""Batch normalization layer."""

def __init__(self, size, eps=0.001):

super().__init__(size)

self.eps = eps

# self.weights = [beta, gamma]

self.weights = [0, 0]

self.inputs = None

def forward(self, inputs):

# mean value of inputs

mu = sum(inputs) / len(inputs)

# standard error of inputs

stderr = statistics.stdev(inputs)

self.inputs = inputs

res = []

# get normalized value of each input

for i in range(len(self.nodes)):

val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.eps + stderr ** 2) + self.weights[1]]

res.append(val)

self.nodes[i].value = val

return res

def init_examples(examples, idx_i, idx_t, o_units):

"""Init examples from dataset.examples."""

inputs, targets = {}, {}

for i, e in enumerate(examples):

# input values of e

inputs[i] = [e[i] for i in idx_i]

if o_units > 1:

# one-hot representation of e's target

t = [0 for i in range(o_units)]

t[e[idx_t]] = 1

targets[i] = t

else:

# target value of e

targets[i] = [e[idx_t]]

return inputs, targets

def stochastic_gradient_descent(dataset, net, loss, epochs=1000, l_rate=0.01, batch_size=1, verbose=False):

"""

Gradient descent algorithm to update the learnable parameters of a network.

:return: the updated network

"""

examples = dataset.examples # init data

for e in range(epochs):

total_loss = 0

random.shuffle(examples)

weights = [[node.weights for node in layer.nodes] for layer in net]

for batch in get_batch(examples, batch_size):

inputs, targets = init_examples(batch, dataset.inputs, dataset.target, len(net[-1].nodes))

# compute gradients of weights

gs, batch_loss = BackPropagation(inputs, targets, weights, net, loss)

# update weights with gradient descent

weights = [x + y for x, y in zip(weights, [np.array(tg) * -l_rate for tg in gs])]

total_loss += batch_loss

# update the weights of network each batch

for i in range(len(net)):

if weights[i].size != 0:

for j in range(len(weights[i])):

net[i].nodes[j].weights = weights[i][j]

if verbose:

print("epoch:{}, total_loss:{}".format(e + 1, total_loss))

return net

def adam(dataset, net, loss, epochs=1000, rho=(0.9, 0.999), delta=1 / 10 ** 8,

l_rate=0.001, batch_size=1, verbose=False):

"""

[Figure 19.6]

Adam optimizer to update the learnable parameters of a network.

Required parameters are similar to gradient descent.

:return the updated network

"""

examples = dataset.examples

# init s,r and t

s = [[[0] * len(node.weights) for node in layer.nodes] for layer in net]

r = [[[0] * len(node.weights) for node in layer.nodes] for layer in net]

t = 0

# repeat util converge

for e in range(epochs):

# total loss of each epoch

total_loss = 0

random.shuffle(examples)

weights = [[node.weights for node in layer.nodes] for layer in net]

for batch in get_batch(examples, batch_size):

t += 1

inputs, targets = init_examples(batch, dataset.inputs, dataset.target, len(net[-1].nodes))

# compute gradients of weights

gs, batch_loss = BackPropagation(inputs, targets, weights, net, loss)

# update s,r,s_hat and r_gat

s = vector_add(scalar_vector_product(rho[0], s),

scalar_vector_product((1 - rho[0]), gs))

r = vector_add(scalar_vector_product(rho[1], r),

scalar_vector_product((1 - rho[1]), element_wise_product(gs, gs)))

s_hat = scalar_vector_product(1 / (1 - rho[0] ** t), s)

r_hat = scalar_vector_product(1 / (1 - rho[1] ** t), r)

# rescale r_hat

r_hat = map_vector(lambda x: 1 / (np.sqrt(x) + delta), r_hat)

# delta weights

delta_theta = scalar_vector_product(-l_rate, element_wise_product(s_hat, r_hat))

weights = vector_add(weights, delta_theta)

total_loss += batch_loss

# update the weights of network each batch

for i in range(len(net)):

if weights[i]:

for j in range(len(weights[i])):

net[i].nodes[j].weights = weights[i][j]

if verbose:

print("epoch:{}, total_loss:{}".format(e + 1, total_loss))

return net

def BackPropagation(inputs, targets, theta, net, loss):

"""

The back-propagation algorithm for multilayer networks in only one epoch, to calculate gradients of theta.

:param inputs: a batch of inputs in an array. Each input is an iterable object

:param targets: a batch of targets in an array. Each target is an iterable object

:param theta: parameters to be updated

:param net: a list of predefined layer objects representing their linear sequence

:param loss: a predefined loss function taking array of inputs and targets

:return: gradients of theta, loss of the input batch

"""

assert len(inputs) == len(targets)

o_units = len(net[-1].nodes)

n_layers = len(net)

batch_size = len(inputs)

gradients = [[[] for _ in layer.nodes] for layer in net]

total_gradients = [[[0] * len(node.weights) for node in layer.nodes] for layer in net]

batch_loss = 0

# iterate over each example in batch

for e in range(batch_size):

i_val = inputs[e]

t_val = targets[e]

# forward pass and compute batch loss

for i in range(1, n_layers):

layer_out = net[i].forward(i_val)

i_val = layer_out

batch_loss += loss(t_val, layer_out)

# initialize delta

delta = [[] for _ in range(n_layers)]

previous = np.array([layer_out[i] - t_val[i] for i in range(o_units)])

h_layers = n_layers - 1

# backward pass

for i in range(h_layers, 0, -1):

layer = net[i]

derivative = np.array([layer.activation.derivative(node.value) for node in layer.nodes])

delta[i] = previous * derivative

# pass to layer i-1 in the next iteration

previous = np.matmul([delta[i]], theta[i])[0]

# compute gradient of layer i

gradients[i] = [scalar_vector_product(d, net[i].inputs) for d in delta[i]]

# add gradient of current example to batch gradient

total_gradients = vector_add(total_gradients, gradients)

return total_gradients, batch_loss

def get_batch(examples, batch_size=1):

"""Split examples into multiple batches"""

for i in range(0, len(examples), batch_size):

yield examples[i: i + batch_size]

class NeuralNetworkLearner:

"""

Simple dense multilayer neural network.

:param hidden_layer_sizes: size of hidden layers in the form of a list

"""

def __init__(self, dataset, hidden_layer_sizes, l_rate=0.01, epochs=1000, batch_size=10,

optimizer=stochastic_gradient_descent, loss=mean_squared_error_loss, verbose=False, plot=False):

self.dataset = dataset

self.l_rate = l_rate

self.epochs = epochs

self.batch_size = batch_size

self.optimizer = optimizer

self.loss = loss

self.verbose = verbose

self.plot = plot

input_size = len(dataset.inputs)

output_size = len(dataset.values[dataset.target])

# initialize the network

raw_net = [InputLayer(input_size)]

# add hidden layers

hidden_input_size = input_size

for h_size in hidden_layer_sizes:

raw_net.append(DenseLayer(hidden_input_size, h_size))

hidden_input_size = h_size

raw_net.append(DenseLayer(hidden_input_size, output_size))

self.raw_net = raw_net

def fit(self, X, y):

self.learned_net = self.optimizer(self.dataset, self.raw_net, loss=self.loss, epochs=self.epochs,

l_rate=self.l_rate, batch_size=self.batch_size, verbose=self.verbose)

return self

def predict(self, example):

n_layers = len(self.learned_net)

layer_input = example

layer_out = example

# get the output of each layer by forward passing

for i in range(1, n_layers):

layer_out = self.learned_net[i].forward(np.array(layer_input).reshape((-1, 1)))

layer_input = layer_out

return layer_out.index(max(layer_out))

class PerceptronLearner:

"""

Simple perceptron neural network.

"""

def __init__(self, dataset, l_rate=0.01, epochs=1000, batch_size=10, optimizer=stochastic_gradient_descent,

loss=mean_squared_error_loss, verbose=False, plot=False):

self.dataset = dataset

self.l_rate = l_rate

self.epochs = epochs

self.batch_size = batch_size

self.optimizer = optimizer

self.loss = loss

self.verbose = verbose

self.plot = plot

input_size = len(dataset.inputs)

output_size = len(dataset.values[dataset.target])

# initialize the network, add dense layer

self.raw_net = [InputLayer(input_size), DenseLayer(input_size, output_size)]

def fit(self, X, y):

self.learned_net = self.optimizer(self.dataset, self.raw_net, loss=self.loss, epochs=self.epochs,

l_rate=self.l_rate, batch_size=self.batch_size, verbose=self.verbose)

return self

def predict(self, example):

layer_out = self.learned_net[1].forward(np.array(example).reshape((-1, 1)))

return layer_out.index(max(layer_out))

def keras_dataset_loader(dataset, max_length=500):

"""

Helper function to load keras datasets.

:param dataset: keras data set type

:param max_length: max length of each input sequence

"""

# init dataset

(X_train, y_train), (X_val, y_val) = dataset

if max_length > 0:

X_train = sequence.pad_sequences(X_train, maxlen=max_length)

X_val = sequence.pad_sequences(X_val, maxlen=max_length)

return (X_train[10:], y_train[10:]), (X_val, y_val), (X_train[:10], y_train[:10])

def SimpleRNNLearner(train_data, val_data, epochs=2, verbose=False):

"""

RNN example for text sentimental analysis.

:param train_data: a tuple of (training data, targets)

Training data: ndarray taking training examples, while each example is coded by embedding

Targets: ndarray taking targets of each example. Each target is mapped to an integer

:param val_data: a tuple of (validation data, targets)

:param epochs: number of epochs

:param verbose: verbosity mode

:return: a keras model

"""

total_inputs = 5000

input_length = 500

# init data

X_train, y_train = train_data

X_val, y_val = val_data

# init a the sequential network (embedding layer, rnn layer, dense layer)

model = Sequential()

model.add(Embedding(total_inputs, 32, input_length=input_length))

model.add(SimpleRNN(units=128))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# train the model

model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=epochs, batch_size=128, verbose=verbose)

return model

def AutoencoderLearner(inputs, encoding_size, epochs=200, verbose=False):

"""

Simple example of linear auto encoder learning producing the input itself.

:param inputs: a batch of input data in np.ndarray type

:param encoding_size: int, the size of encoding layer

:param epochs: number of epochs

:param verbose: verbosity mode

:return: a keras model

"""

# init data

input_size = len(inputs[0])

# init model

model = Sequential()

model.add(Dense(encoding_size, input_dim=input_size, activation='relu', kernel_initializer='random_uniform',

bias_initializer='ones'))

model.add(Dense(input_size, activation='relu', kernel_initializer='random_uniform', bias_initializer='ones'))

# update model with sgd

sgd = optimizers.SGD(lr=0.01)

model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['accuracy'])

# train the model

model.fit(inputs, inputs, epochs=epochs, batch_size=10, verbose=verbose)

return model

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