deeplearning.ai 第四课第二周 resnet 50层神经网络实现

1、导入函数库:

import numpy as npfrom keras import layersfrom keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2Dfrom keras.models import Model, load_modelfrom keras.preprocessing import imagefrom keras.utils import layer_utilsfrom keras.utils.data_utils import get_filefrom keras.applications.imagenet_utils import preprocess_inputimport pydotfrom IPython.display import SVGfrom keras.utils.vis_utils import model_to_dotfrom keras.utils import plot_modelfrom resnets_utils import *from keras.initializers import glorot_uniformimport scipy.miscfrom matplotlib.pyplot import imshow%matplotlib inlineimport keras.backend as KK.set_image_data_format('channels_last')K.set_learning_phase(1)

2、定义一个identity_block，该block的特质是其在一个block内，四层的矩阵维度都没有变化
block如下图所示：

# GRADED FUNCTION: identity_blockdef 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 = Add()([X,X_shortcut])    X = Activation('relu')(X)    ### END CODE HERE ###    return X

3、定义convolution_block 该定义是，该block跳跃两层，总共有四层，且中间传输数据维度有变化。图片如下

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), 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),name=conv_name_base+'2b',padding='same',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),name=conv_name_base+'2c',padding='valid',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),name=conv_name_base+'1',padding='valid',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 = Add()([X,X_shortcut])    X = Activation('relu')(X)    ### END CODE HERE ###    return X

4、建立50层的resnet，相应构图及要求如下：

# GRADED FUNCTION: ResNet50def 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,f=3,filters=[128,128,512],stage=3,block='a',s=2)    X = identity_block(X,f=3,filters=[128,128,512],stage=3,block='b')    X = identity_block(X,f=3,filters=[128,128,512],stage=3,block='c')    X = identity_block(X,f=3,filters=[128,128,512],stage=3,block='d')    # Stage 4 (≈6 lines)    X = convolutional_block(X,f = 3, filters=[256,256,1024],stage=4,block='a',s=2)    X = identity_block(X,f=3,filters=[256,256,1024],stage=4,block='b')    X = identity_block(X,f=3,filters=[256,256,1024],stage=4,block='c')    X = identity_block(X,f=3,filters=[256,256,1024],stage=4,block='d')    X = identity_block(X,f=3,filters=[256,256,1024],stage=4,block='e')    X = identity_block(X,f=3,filters=[256,256,1024],stage=4,block='f')    # Stage 5 (≈3 lines)    X = convolutional_block(X,f=3,filters=[512,512,2048],stage=5,block='a',s=2)    X = identity_block(X,f=3,filters=[512,512,2048],stage=5,block='b')    X = identity_block(X,f=3,filters=[512,512,2048],stage=5,block='c')    # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"    X = AveragePooling2D(pool_size=(2,2),strides=(1,1),padding='valid')(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

5、模型实体化：

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

6、定义模型训练过程及相应超参 （compile）

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

7、训练数据载入：

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Normalize image vectorsX_train = X_train_orig/255.X_test = X_test_orig/255.# Convert training and test labels to one hot matricesY_train = convert_to_one_hot(Y_train_orig, 6).TY_test = convert_to_one_hot(Y_test_orig, 6).Tprint ("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))

8、模型训练：

model.fit(X_train, Y_train, epochs = 100, batch_size = 32)

输出如下：

9、模型评估：

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