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build.py
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build.py
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#!/usr/bin/env python
"""
Author: Shashank Kotyan
Email: [email protected]
"""
import os, traceback, random, numpy as np
import networkx as nx
from matplotlib import pyplot as plt
from process import Process
x = np.zeros((1,32,32,3))
y = np.zeros((1,10))
y[0] = 1
def plot_graph(individual, dna):
G= dna['graph']
dense_nodes, conv_nodes, mandatory_nodes = [], [], []
dense_nodes_color, conv_nodes_color = [], []
cmap = plt.cm.get_cmap('Blues', 4)
dmap = plt.cm.get_cmap('Blues', 4)
for node in G.nodes:
layer_graph = G.nodes[node]
if layer_graph['type_layer'] == 'fully_connected':
dense_nodes += [node]
if layer_graph['use_bn'] and layer_graph['use_dropout'][0]: dense_nodes_color += [dmap(1)]
elif layer_graph['use_bn'] and not layer_graph['use_dropout'][0]: dense_nodes_color += [dmap(2)]
elif not layer_graph['use_bn'] and layer_graph['use_dropout'][0]: dense_nodes_color += [dmap(3)]
else: dense_nodes_color += [dmap(4)]
elif layer_graph['type_layer'] == 'convolution_2d':
conv_nodes += [node]
if layer_graph['use_bn'] and layer_graph['use_dropout'][0]: conv_nodes_color += [cmap(1)]
elif layer_graph['use_bn'] and not layer_graph['use_dropout'][0]: conv_nodes_color += [cmap(2)]
elif not layer_graph['use_bn'] and layer_graph['use_dropout'][0]: conv_nodes_color += [cmap(3)]
else: conv_nodes_color += [cmap(4)]
else:
mandatory_nodes += [node]
layout = ['random', 'circular', 'shell', 'spring', 'spectral', 'kamada_kawai', 'planar', 'spiral']
for i in range(len(layout)):
try:
if i == 0: pos = nx.random_layout(G)
elif i == 1: pos = nx.circular_layout(G)
elif i == 2: pos = nx.shell_layout(G)
elif i == 3: pos = nx.spring_layout(G)
elif i == 4: pos = nx.spectral_layout(G)
elif i == 5: pos = nx.kamada_kawai_layout(G)
elif i == 6: pos = nx.planar_layout(G)
elif i == 7: pos = nx.spiral_layout(G)
nx.draw_networkx_nodes(G, pos, nodelist=dense_nodes, node_size=300, node_color=dense_nodes_color, node_shape='o', cmap=dmap)
nx.draw_networkx_nodes(G, pos, nodelist=conv_nodes, node_size=300, node_color=conv_nodes_color, node_shape='s', cmap=cmap)
nx.draw_networkx_nodes(G, pos, nodelist=mandatory_nodes, node_size=300, node_color='black', node_shape='h')
nx.draw_networkx_edges(G, pos)
plt.savefig(f"{individual}/network_{layout[i]}.png", bbox='tight', dpi=300)
plt.clf()
except Exception as e:
pass
def build_block(G, num_classes=1, gpu_index=None):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if gpu_index is not None: os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_index)
else: os.environ["CUDA_VISIBLE_DEVICES"] = ""
import tensorflow as tf
if gpu_index is not None:
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
except RuntimeError as e: print(e)
from tensorflow.keras import callbacks, datasets, utils, layers, models, optimizers, backend as K
from tensorflow.keras.preprocessing.image import ImageDataGenerator
keras_layers = {}
input_node, output_node = 0, 1
keras_layers[input_node] = layers.Input(shape=(32,32,3))
keras_layers[output_node] = layers.GlobalAveragePooling2D()
for node in G.nodes:
layer_graph = G.nodes[node]
if layer_graph['type_layer'] in ['fully_connected', 'convolution_2d']:
layer_params = layer_graph['layer_params']
if layer_graph['type_layer'] == 'convolution_2d': layer = layers.Conv2D(filters=layer_params[0], kernel_size=(layer_params[1], layer_params[2]), strides=(layer_params[3], layer_params[4]), padding='same')
elif layer_graph['type_layer'] == 'fully_connected': layer = layers.Dense(units=layer_params[0])
keras_layers[node] = layer
explored, queue = [input_node], [input_node]
while queue:
node = queue.pop(0)
for successor in G.successors(node):
add = True
for predecessor in G.predecessors(successor):
if predecessor not in explored: add = False
if add: queue.append(successor)
if node not in explored:
predecessors = [predecessor for predecessor in G.predecessors(node)]
if len(predecessors) == 1:
if predecessors[0] == 0: ilayer = layers.Lambda(lambda x: x)(keras_layers[predecessors[0]])
else: ilayer = keras_layers[predecessors[0]]
elif len(predecessors) > 1:
ilayer = layers.Concatenate()([layers.Flatten()(keras_layers[predecessor]) for predecessor in predecessors])
shape = ilayer.shape[1]
if shape % 65536 == 0: ilayer = layers.Reshape((256, 256, shape // 65536))(ilayer)
elif shape % 16384 == 0: ilayer = layers.Reshape((128, 128, shape // 16384))(ilayer)
elif shape % 4096 == 0: ilayer = layers.Reshape((64, 64, shape // 4096))(ilayer)
elif shape % 1024 == 0: ilayer = layers.Reshape((32, 32, shape // 1024))(ilayer)
elif shape % 256 == 0: ilayer = layers.Reshape((16, 16, shape // 256))(ilayer)
elif shape % 64 == 0: ilayer = layers.Reshape((8, 8, shape // 64))(ilayer)
elif shape % 16 == 0: ilayer = layers.Reshape((4, 4, shape // 16))(ilayer)
elif shape % 4 == 0: ilayer = layers.Reshape((2, 2, shape // 4))(ilayer)
else: ilayer = layers.Reshape((1, 1, shape))(ilayer)
keras_layers[node] = keras_layers[node](ilayer)
layer_graph = G.nodes[node]
if layer_graph['type_layer'] != 'output':
keras_layers[node] = layers.BatchNormalization()(keras_layers[node])
keras_layers[node] = layers.Activation('relu')(keras_layers[node])
explored.append(node)
for successor in G.successors(input_node):
add = True
for predecessor in G.predecessors(successor):
if predecessor not in explored: add = False
if add: queue.append(successor)
else: waiting.append(successor)
assert set(explored) == set(list(G.nodes))
layer = layers.Dense(units=10, activation='softmax', name='Output')(keras_layers[output_node])
model = models.Model(inputs=keras_layers[input_node], outputs=layer)
model.compile(optimizer=optimizers.Nadam(), loss='categorical_crossentropy', metrics=['accuracy'])
try: model.fit(x, y, epochs=1, verbose=0)
except Exception as e : raise Exception(f"Model cannot be trained: {e} \n{traceback.format_exc()}\n")
return model
def check_block(graph):
p = Process(target=build_block, args=(graph, 1, None))
p.start()
p.join()
if p.exception is not None: raise Exception(f"{p.exception[0]}, {p.exception[1]}")
def build_keras_block(graph,individual, fitness_string):
from tensorflow.keras.utils import plot_model
plot_model(build_block(graph, 1, None), to_file=f"{individual}/keras_graph_{fitness_string}.png", dpi=300)
def check_keras_block(graph, individual, fitness_string):
p = Process(target=build_keras_block, args=(graph, individual, fitness_string))
p.start()
p.join()
if p.exception is not None: raise Exception(f"{p.exception[0]}, {p.exception[1]}")
def load_data(dataset):
from tensorflow.keras import callbacks, datasets, utils
from tensorflow.keras.preprocessing.image import ImageDataGenerator
def preprocess(x):
for i in range(3): x[:,:,:,i] = (x[:,:,:,i] - mean[i]) / std[i]
return x
if dataset == 0:
num_classes = 10
(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()
x_train, x_test = x_train.reshape(-1, 28, 28, 1).astype('float32'), x_test.reshape(-1, 28, 28, 1).astype('float32')
y_train, y_test = utils.to_categorical(y_train, 10), utils.to_categorical(y_test, 10)
elif dataset == 1:
num_classes = 10
(x_train, y_train), (x_test, y_test) = datasets.fashion_mnist.load_data()
x_train, x_test = x_train.reshape(-1, 28, 28, 1).astype('float32'), x_test.reshape(-1, 28, 28, 1).astype('float32')
y_train, y_test = utils.to_categorical(y_train, 10), utils.to_categorical(y_test, 10)
elif dataset == 2:
num_classes = 10
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
x_train, x_test = x_train.astype('float32'), x_test.astype('float32')
y_train, y_test = utils.to_categorical(y_train[:,0], 10), utils.to_categorical(y_test[:,0], 10)
elif dataset == 3:
num_classes = 100
(x_train, y_train), (x_test, y_test) = datasets.cifar100.load_data()
x_train, x_test = x_train.astype('float32'), x_test.astype('float32')
mean, std = [125.307, 122.95, 113.865], [62.9932, 62.0887, 66.7048]
x_train, x_test = preprocess(x_train.astype('float32')), preprocess(x_test.astype('float32'))
y_train, y_test = utils.to_categorical(y_train[:,0], 100), utils.to_categorical(y_test[:,0], 100)
cbks = []
cbks += [callbacks.EarlyStopping(monitor='accuracy', min_delta=0.0001, patience=15)]
datagen = ImageDataGenerator(horizontal_flip=True, width_shift_range=0.125, height_shift_range=0.125, fill_mode='constant', cval=0.)
datagen.fit(x_train)
return {
'callbacks':cbks, 'datagen': datagen,
'x_train': x_train, 'x_test': x_test, 'y_train': y_train, 'y_test': y_test,
'count_train': len(x_train), 'count_test': len(x_test),
'num_classes': num_classes
}