Andrew Ng-Convolutional Neural Networks 第二周作业 Residual Networks

来到残差网络了。这周的作业最终是一个50层的残差网络，作业上说明了，只会进行两次迭代来测试代码，然而这两次就已经花了近十分钟的时间，所以如果要做完完整的二十次迭代要上小时（using CPU）。。所以后面就附带了他们已经训练好的二十次迭代之后的结果给我们演示一次结果……最终的准确率是86%。

放代码：
import numpy as np
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from keras.models import Model, load_model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from resnets_utils import *
from keras.initializers import glorot_uniform
import scipy.misc
from matplotlib.pyplot import imshow
%matplotlib inline

import keras.backend as K
K.set_image_data_format('channels_last')
K.set_learning_phase(1)

# GRADED FUNCTION: identity_block

def identity_block(X, f, filters, stage, block):
"""
Implementation of the identity block as defined in Figure 3

Arguments:
X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
f -- integer, specifying the shape of the middle CONV's window for the main path
filters -- python list of integers, defining the number of filters in the CONV layers of the main path
stage -- integer, used to name the layers, depending on their position in the network
block -- string/character, used to name the layers, depending on their position in the network

Returns:
X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
"""

# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'

# Retrieve Filters
F1, F2, F3 = filters

# Save the input value. You'll need this later to add back to the main path.
X_shortcut = X

# First component of main path
X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)

### START CODE HERE ###

# Second component of main path (≈3 lines)
X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)
X = Activation('relu')(X)

# Third component of main path (≈2 lines)
X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)

# Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
X = layers.add([X, X_shortcut])
X = Activation('relu')(X)

### END CODE HERE ###

return X

tf.reset_default_graph()

with tf.Session() as test:
np.random.seed(1)
A_prev = tf.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = " + str(out[0][1][1][0]))

# GRADED FUNCTION: convolutional_block

def convolutional_block(X, f, filters, stage, block, s = 2):
"""
Implementation of the convolutional block as defined in Figure 4

Arguments:
X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
f -- integer, specifying the shape of the middle CONV's window for the main path
filters -- python list of integers, defining the number of filters in the CONV layers of the main path
stage -- integer, used to name the layers, depending on their position in the network
block -- string/character, used to name the layers, depending on their position in the network
s -- Integer, specifying the stride to be used

Returns:
X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
"""

# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'

# Retrieve Filters
F1, F2, F3 = filters

# Save the input value
X_shortcut = X

##### MAIN PATH #####
# First component of main path
X = Conv2D(F1, (1, 1), strides = (s,s), padding='valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)

### START CODE HERE ###

# Second component of main path (≈3 lines)
X = Conv2D(F2, (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)
X = Activation('relu')(X)

# Third component of main path (≈2 lines)
X = Conv2D(F3, (1, 1), strides = (1,1), padding='valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)

##### SHORTCUT PATH #### (≈2 lines)
X_shortcut = Conv2D(F3, (1, 1), strides = (s,s), padding='valid', name = conv_name_base + '1', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)
X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)

# Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
X = layers.add([X, X_shortcut])
X = Activation('relu')(X)

### END CODE HERE ###

return X

tf.reset_default_graph()

with tf.Session() as test:
np.random.seed(1)
A_prev = tf.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = " + str(out[0][1][1][0]))

# GRADED FUNCTION: ResNet50

def ResNet50(input_shape = (64, 64, 3), classes = 6):
"""
Implementation of the popular ResNet50 the following architecture:
CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
-> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER

Arguments:
input_shape -- shape of the images of the dataset
classes -- integer, number of classes

Returns:
model -- a Model() instance in Keras
"""

# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)

# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)

# Stage 1
X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)
X = Activation('relu')(X)
X = MaxPooling2D((3, 3), strides=(2, 2))(X)

# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)
X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')

### START CODE HERE ###

# Stage 3 (≈4 lines)
X = convolutional_block(X, 3, [128,128,512], 3, "a", s = 2)
X = identity_block(X, 3, [128,128,512],3, "b")
X = identity_block(X, 3, [128,128,512], 3, "c")
X = identity_block(X, 3, [128,128,512], 3, "d")
# Stage 4 (≈6 lines)
X = convolutional_block(X, 3, [256, 256, 1024], 4, "a", s = 2)
X = identity_block(X, 3, [256, 256, 1024],4, "b")
X = identity_block(X, 3, [256, 256, 1024],4, "c")
X = identity_block(X, 3, [256, 256, 1024],4, "d")
X = identity_block(X, 3, [256, 256, 1024],4, "e")
X = identity_block(X, 3, [256, 256, 1024],4, "f")
# Stage 5 (≈3 lines)
X = convolutional_block(X, 3, [512, 512, 2048], 5, "a", s = 2)
X = identity_block(X, 3,[512, 512, 2048],5, "b")
X = identity_block(X, 3, [512, 512, 2048],5, "c")

# AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
X =AveragePooling2D(pool_size=(2,2))(X)

### END CODE HERE ###

# output layer
X = Flatten()(X)
X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)

# Create model
model = Model(inputs = X_input, outputs = X, name='ResNet50')

return model

model = ResNet50(input_shape = (64, 64, 3), classes = 6)

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

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Normalize image vectors
X_train =
9df1
X_train_orig/255.
X_test = X_test_orig/255.

# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T

print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))

model.fit(X_train, Y_train, epochs = 2, batch_size = 32)

preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

model = load_model('ResNet50.h5')

preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))