import tensorflow as tf import os import numpy as np from matplotlib import pyplot as plt from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense from tensorflow.keras import Model np.set_printoptions(threshold=np.inf) cifar10 = tf.keras.datasets.cifar10 (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 class VGG16(Model): def __init__(self): super(VGG16, self).__init__() self.c1 = Conv2D(filters=64, kernel_size=(3, 3), padding='same') # 卷积层1 self.b1 = BatchNormalization() # BN层1 self.a1 = Activation('relu') # 激活层1 self.c2 = Conv2D(filters=64, kernel_size=(3, 3), padding='same', ) self.b2 = BatchNormalization() # BN层1 self.a2 = Activation('relu') # 激活层1 self.p1 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same') self.d1 = Dropout(0.2) # dropout层 self.c3 = Conv2D(filters=128, kernel_size=(3, 3), padding='same') self.b3 = BatchNormalization() # BN层1 self.a3 = Activation('relu') # 激活层1 self.c4 = Conv2D(filters=128, kernel_size=(3, 3), padding='same') self.b4 = BatchNormalization() # BN层1 self.a4 = Activation('relu') # 激活层1 self.p2 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same') self.d2 = Dropout(0.2) # dropout层 self.c5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same') self.b5 = BatchNormalization() # BN层1 self.a5 = Activation('relu') # 激活层1 self.c6 = Conv2D(filters=256, kernel_size=(3, 3), padding='same') self.b6 = BatchNormalization() # BN层1 self.a6 = Activation('relu') # 激活层1 self.c7 = Conv2D(filters=256, kernel_size=(3, 3), padding='same') self.b7 = BatchNormalization() self.a7 = Activation('relu') self.p3 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same') self.d3 = Dropout(0.2) self.c8 = Conv2D(filters=512, kernel_size=(3, 3), padding='same') self.b8 = BatchNormalization() # BN层1 self.a8 = Activation('relu') # 激活层1 self.c9 = Conv2D(filters=512, kernel_size=(3, 3), padding='same') self.b9 = BatchNormalization() # BN层1 self.a9 = Activation('relu') # 激活层1 self.c10 = Conv2D(filters=512, kernel_size=(3, 3), padding='same') self.b10 = BatchNormalization() self.a10 = Activation('relu') self.p4 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same') self.d4 = Dropout(0.2) self.c11 = Conv2D(filters=512, kernel_size=(3, 3), padding='same') self.b11 = BatchNormalization() # BN层1 self.a11 = Activation('relu') # 激活层1 self.c12 = Conv2D(filters=512, kernel_size=(3, 3), padding='same') self.b12 = BatchNormalization() # BN层1 self.a12 = Activation('relu') # 激活层1 self.c13 = Conv2D(filters=512, kernel_size=(3, 3), padding='same') self.b13 = BatchNormalization() self.a13 = Activation('relu') self.p5 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same') self.d5 = Dropout(0.2) self.flatten = Flatten() self.f1 = Dense(512, activation='relu') self.d6 = Dropout(0.2) self.f2 = Dense(512, activation='relu') self.d7 = Dropout(0.2) self.f3 = Dense(10, activation='softmax') def call(self, x): x = self.c1(x) x = self.b1(x) x = self.a1(x) x = self.c2(x) x = self.b2(x) x = self.a2(x) x = self.p1(x) x = self.d1(x) x = self.c3(x) x = self.b3(x) x = self.a3(x) x = self.c4(x) x = self.b4(x) x = self.a4(x) x = self.p2(x) x = self.d2(x) x = self.c5(x) x = self.b5(x) x = self.a5(x) x = self.c6(x) x = self.b6(x) x = self.a6(x) x = self.c7(x) x = self.b7(x) x = self.a7(x) x = self.p3(x) x = self.d3(x) x = self.c8(x) x = self.b8(x) x = self.a8(x) x = self.c9(x) x = self.b9(x) x = self.a9(x) x = self.c10(x) x = self.b10(x) x = self.a10(x) x = self.p4(x) x = self.d4(x) x = self.c11(x) x = self.b11(x) x = self.a11(x) x = self.c12(x) x = self.b12(x) x = self.a12(x) x = self.c13(x) x = self.b13(x) x = self.a13(x) x = self.p5(x) x = self.d5(x) x = self.flatten(x) x = self.f1(x) x = self.d6(x) x = self.f2(x) x = self.d7(x) y = self.f3(x) return y model = VGG16() model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['sparse_categorical_accuracy']) checkpoint_save_path = "./checkpoint/VGG16.ckpt" if os.path.exists(checkpoint_save_path + '.index'): print('-------------load the model-----------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True, save_best_only=True) history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1, callbacks=[cp_callback]) model.summary() # print(model.trainable_variables) file = open('./weights.txt', 'w') for v in model.trainable_variables: file.write(str(v.name) + '\n') file.write(str(v.shape) + '\n') file.write(str(v.numpy()) + '\n') file.close() ############################################### show ############################################### # 显示训练集和验证集的acc和loss曲线 acc = history.history['sparse_categorical_accuracy'] val_acc = history.history['val_sparse_categorical_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] plt.subplot(1, 2, 1) plt.plot(acc, label='Training Accuracy') plt.plot(val_acc, label='Validation Accuracy') plt.title('Training and Validation Accuracy') plt.legend() plt.subplot(1, 2, 2) plt.plot(loss, label='Training Loss') plt.plot(val_loss, label='Validation Loss') plt.title('Training and Validation Loss') plt.legend() plt.show()