我就廢話不多說了，大家還是直接看代碼吧~
''' Created on 2018-4-16 ''' import keras from keras.models import Sequential from keras.layers import Dense from keras.models import Model from keras.callbacks import ModelCheckpoint,Callback import numpy as np import tflearn import tflearn.datasets.mnist as mnist x_train, y_train, x_test, y_test = mnist.load_data(one_hot=True) x_valid = x_test[:5000] y_valid = y_test[:5000] x_test = x_test[5000:] y_test = y_test[5000:] print(x_valid.shape) print(x_test.shape) model = Sequential() model.add(Dense(units=64, activation='relu', input_dim=784)) model.add(Dense(units=10, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) filepath = 'D:machineTestmodel-ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5' # filepath = 'D:machineTestmodel-ep{epoch:03d}-loss{loss:.3f}.h5' checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min') print(model.get_config()) # [{'class_name': 'Dense', 'config': {'bias_regularizer': None, 'use_bias': True, 'kernel_regularizer': None, 'batch_input_shape': (None, 784), 'trainable': True, 'kernel_constraint': None, 'bias_constraint': None, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'distribution': 'uniform', 'mode': 'fan_avg', 'seed': None}}, 'activity_regularizer': None, 'units': 64, 'dtype': 'float32', 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'activation': 'relu', 'name': 'dense_1'}}, {'class_name': 'Dense', 'config': {'bias_regularizer': None, 'use_bias': True, 'kernel_regularizer': None, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_constraint': None, 'bias_constraint': None, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'distribution': 'uniform', 'mode': 'fan_avg', 'seed': None}}, 'activity_regularizer': None, 'trainable': True, 'units': 10, 'activation': 'softmax', 'name': 'dense_2'}}] # model.fit(x_train, y_train, epochs=1, batch_size=128, callbacks=[checkpoint],validation_data=(x_valid, y_valid)) model.fit(x_train, y_train, epochs=1,validation_data=(x_valid, y_valid),steps_per_epoch=10,validation_steps=1) # score = model.evaluate(x_test, y_test, batch_size=128) # print(score) # #獲取模型結構狀況 # model.summary() # _________________________________________________________________ # Layer (type) Output Shape Param # # ================================================================= # dense_1 (Dense) (None, 64) 50240(784*64+64(b)) # _________________________________________________________________ # dense_2 (Dense) (None, 10) 650(64*10 + 10 ) # ================================================================= # #根據下標和名稱返回層對象 # layer = model.get_layer(index = 0) # 獲取模型權重,設置權重model.set_weights() weights = np.array(model.get_weights()) print(weights.shape) # (4,)權重由4部分組成 print(weights[0].shape) # (784, 64)dense_1 w1 print(weights[1].shape) # (64,)dense_1 b1 print(weights[2].shape) # (64, 10)dense_2 w2 print(weights[3].shape) # (10,)dense_2 b2 # # 保存權重和加載權重 # model.save_weights("D:xxxweights.h5") # model.load_weights("D:xxxweights.h5", by_name=False)#by_name=True，可以根據名字匹配和層載入權重 # 查看中間結果，必須要先聲明個函數式模型 dense1_layer_model = Model(inputs=model.input,outputs=model.get_layer('dense_1').output) out = dense1_layer_model.predict(x_test) print(out.shape) # (5000, 64) # 如果是函數式模型，則可以直接輸出 # import keras # from keras.models import Model # from keras.callbacks import ModelCheckpoint,Callback # import numpy as np # from keras.layers import Input,Conv2D,MaxPooling2D # import cv2 # # image = cv2.imread("D:machineTestfalali.jpg") # print(image.shape) # cv2.imshow("1",image) # # # 第一層conv # image = image.reshape([-1, 386, 580, 3]) # img_input = Input(shape=(386, 580, 3)) # x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input) # x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) # x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # model = Model(inputs=img_input, outputs=x) # out = model.predict(image) # print(out.shape) # out = out.reshape(193, 290,64) # image_conv1 = out[:,:,1].reshape(193, 290) # image_conv2 = out[:,:,20].reshape(193, 290) # image_conv3 = out[:,:,40].reshape(193, 290) # image_conv4 = out[:,:,60].reshape(193, 290) # cv2.imshow("conv1",image_conv1) # cv2.imshow("conv2",image_conv2) # cv2.imshow("conv3",image_conv3) # cv2.imshow("conv4",image_conv4) # cv2.waitKey(0)
中間結果輸出可以查看conv過之後的圖像：
原始圖像：
經過一層conv以後,輸出其中4張圖片：
[zmcjlove ] keras 模型參數,模型保存,中間結果輸出操作已經有454次圍觀