《Web安全之深度学习实战》笔记：第一章 深度学习工具箱

本小节通过keras和tensorflow识别mnist数据集，来讲述基本用法。

def use_keras():
    import keras
    from keras.datasets import mnist
    from keras.models import Sequential
    from keras.layers import Dense, Dropout
    from keras.optimizers import RMSprop

    batch_size = 128
    num_classes = 10
    epochs = 20

    # the data, shuffled and split between train and test sets
    (x_train, y_train), (x_test, y_test) = mnist.load_data('d://web/data/mnist/mnist.npz')

    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    model = Sequential()
    model.add(Dense(512, activation='relu', input_shape=(784,)))
    model.add(Dropout(0.2))
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(10, activation='softmax'))

    model.summary()

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

    history = model.fit(x_train, y_train,
                        batch_size=batch_size,
                        epochs=epochs,
                        verbose=1,
                        validation_data=(x_test, y_test))
    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

结构如下所示

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 512)               401920    
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 512)               262656    
_________________________________________________________________
dropout_2 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 10)                5130      
=================================================================
Total params: 669,706
Trainable params: 669,706
Non-trainable params: 0

 运行结果

60000/60000 [==============================] - 8s 136us/step - loss: 0.0173 - accuracy: 0.9954 - val_loss: 0.1279 - val_accuracy: 0.9836
Test loss: 0.12785854045796335

def use_tflearn():
    import tflearn

    # Data loading and preprocessing
    import tflearn.datasets.mnist as mnist
    X, Y, testX, testY = mnist.load_data(one_hot=True)

    # Building deep neural network
    input_layer = tflearn.input_data(shape=[None, 784])
    dense1 = tflearn.fully_connected(input_layer, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout1 = tflearn.dropout(dense1, 0.8)
    dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout2 = tflearn.dropout(dense2, 0.8)
    softmax = tflearn.fully_connected(dropout2, 10, activation='softmax')

    # Regression using SGD with learning rate decay and Top-3 accuracy
    sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
    top_k = tflearn.metrics.Top_k(3)
    net = tflearn.regression(softmax, optimizer=sgd, metric=top_k,
                             loss='categorical_crossentropy')

    # Training
    model = tflearn.DNN(net, tensorboard_verbose=0)
    model.fit(X, Y, n_epoch=20, validation_set=(testX, testY),
              show_metric=True, run_id="dense_model")

运行结果

Training Step: 17200  | total loss: 0.40818 | time: 6.170s
| SGD | epoch: 020 | loss: 0.40818 - top3: 0.9742 | val_loss: 0.11193 - val_top3: 0.9947 -- iter: 55000/55000