本次的完整代码已上传至github：https://github.com/CrazyKKK/course4_week2-2
对于其中一些不懂得函数方法，我已加上了注释。
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 import keras.backend as K K.set_image_data_format('channels_last') K.set_learning_phase(1) #1为训练模式，0为工作模式 K.clear_session() """ Implement the ResNet identity block: First component of main path: - The first CONV2D has F1 filters of shape (1,1) and a stride of (1,1). Its padding is “valid” and its name should be conv_name_base + '2a'. Use 0 as the seed for the random initialization. - The first BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2a'. - Then apply the ReLU activation function. This has no name and no hyperparameters. Second component of main path: - The second CONV2D has F2 filters of shape (f,f) and a stride of (1,1). Its padding is “same” and its name should be conv_name_base + '2b'. Use 0 as the seed for the random initialization. - The second BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2b'. - Then apply the ReLU activation function. This has no name and no hyperparameters. Third component of main path: - The third CONV2D has F3 filters of shape (1,1) and a stride of (1,1). Its padding is “valid” and its name should be conv_name_base + '2c'. Use 0 as the seed for the random initialization. - The third BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2c'. Note that there is no ReLU activation function in this component. Final step: - The shortcut and the input are added together. - Then apply the ReLU activation function. This has no name and no hyperparameters. """ # 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 #Glorot均匀分布初始化方法，又成Xavier均匀初始化，参数从[-limit, limit]的均匀分布产生，其中limit为sqrt(6 / (fan_in + fan_out))。 # fan_in为权值张量的输入单元数，fan_out是权重张量的输出单元数。 # 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) # Second component of main path 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 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 X = Add()([X,X_shortcut]) X = Activation('relu')(X) 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])) """ """ The details of the convolutional block are as follows. First component of main path: - The first CONV2D has F1 filters of shape (1,1) and a stride of (s,s). Its padding is “valid” and its name should be conv_name_base + '2a'. - The first BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2a'. - Then apply the ReLU activation function. This has no name and no hyperparameters. Second component of main path: - The second CONV2D has F2 filters of (f,f) and a stride of (1,1). Its padding is “same” and it’s name should be conv_name_base + '2b'. - The second BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2b'. - Then apply the ReLU activation function. This has no name and no hyperparameters. Third component of main path: - The third CONV2D has F3 filters of (1,1) and a stride of (1,1). Its padding is “valid” and it’s name should be conv_name_base + '2c'. - The third BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2c'. Note that there is no ReLU activation function in this component. Shortcut path: - The CONV2D has F3 filters of shape (1,1) and a stride of (s,s). Its padding is “valid” and its name should be conv_name_base + '1'. - The BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '1'. Final step: - The shortcut and the main path values are added together. - Then apply the ReLU activation function. This has no name and no hyperparameters. """ # 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) + '_branch' bn_name_base = 'bn' + str(stage) + '_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) # 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 X = Conv2D(F3,(1,1),strides=(1,1),name=conv_name_base + '2c',kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3,name=bn_name_base + '2c')(X) ##### SHORTCUT PATH #### X_shortcut = Conv2D(F3,(1,1),strides=(s,s),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 X = Add()([X, X_shortcut]) X = Activation('relu')(X) 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])) ''' #Building your first ResNet model (50 layers) """ The details of this ResNet-50 model are: - Zero-padding pads the input with a pad of (3,3) - Stage 1: - The 2D Convolution has 64 filters of shape (7,7) and uses a stride of (2,2). Its name is “conv1”. - BatchNorm is applied to the channels axis of the input. - MaxPooling uses a (3,3) window and a (2,2) stride. - Stage 2: - The convolutional block uses three set of filters of size [64,64,256], “f” is 3, “s” is 1 and the block is “a”. - The 2 identity blocks use three set of filters of size [64,64,256], “f” is 3 and the blocks are “b” and “c”. - Stage 3: - The convolutional block uses three set of filters of size [128,128,512], “f” is 3, “s” is 2 and the block is “a”. - The 3 identity blocks use three set of filters of size [128,128,512], “f” is 3 and the blocks are “b”, “c” and “d”. - Stage 4: - The convolutional block uses three set of filters of size [256, 256, 1024], “f” is 3, “s” is 2 and the block is “a”. - The 5 identity blocks use three set of filters of size [256, 256, 1024], “f” is 3 and the blocks are “b”, “c”, “d”, “e” and “f”. - Stage 5: - The convolutional block uses three set of filters of size [512, 512, 2048], “f” is 3, “s” is 2 and the block is “a”. - The 2 identity blocks use three set of filters of size [512, 512, 2048], “f” is 3 and the blocks are “b” and “c”. - The 2D Average Pooling uses a window of shape (2,2) and its name is “avg_pool”. - The flatten doesn’t have any hyperparameters or name. - The Fully Connected (Dense) layer reduces its input to the number of classes using a softmax activation. Its name should be 'fc' + str(classes). """ # 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_shap # 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, 3, [128, 128, 512], stage=3, block='b') X = identity_block(X, 3, [128, 128, 512], stage=3, block='c') X = identity_block(X, 3, [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, 3, [256, 256, 1024], stage=4, block='b') X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c') X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d') X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e') X = identity_block(X, 3, [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, 3, [512, 512, 2048], stage=5, block='b') X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c') # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)" X = AveragePooling2D((2, 2), name='avg_pool')(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 = 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 = 5, batch_size = 32) preds = model.evaluate(X_test, Y_test) print ("MyLoss = " + str(preds[0])) print ("MyTest Accuracy = " + str(preds[1])) """ model = load_model('ResNet50.h5') preds = model.evaluate(X_test, Y_test) print ("NgLoss = " + str(preds[0])) print ("NgTest Accuracy = " + str(preds[1])) """ img_path = 'images/my_image.jpg' img = image.load_img(img_path, target_size=(64, 64)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) print('Input image shape:', x.shape) my_image = scipy.misc.imread(img_path) imshow(my_image) print("class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ") print(model.predict(x))