keras：1)初體驗-MLP神經網絡實現MNIST手寫識別

Keras是基於Theano和TensorFlow的深度學習庫，具體介紹請參考
官方網：https://keras.io/
中文文檔：http://keras-cn.readthedocs.io/en/latest/

先貼出官方在Git的源碼例子minist_mlp，可以和之前對應的tensorflow博文tensorflow：1)簡單的神經網絡對比學習

'''Trains a simple deep NN on the MNIST dataset.

Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''

from __future__ import print_function

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()

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')
#除以255，把數據正則化到0~1之間
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'])

#verbose=1可以顯示每一步的信息
history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1)
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

Q1:
源碼需要下載10MB大小的MNIST數據集，但是國內好像下載不來(原因，你懂得) ，下面提供mnist.npz下載地址。但注意需放到~.keras\datasets目錄下，比如win10的C:\Users\Javis.keras\datasets

Q2：

Dense(512, activation='relu', input_shape=(784,))

可以先看下這個類的註釋說明：

"""Just your regular densely-connected NN layer.

    `Dense` implements the operation:
    `output = activation(dot(input, kernel) + bias)`
    where `activation` is the element-wise activation function
    passed as the `activation` argument, `kernel` is a weights matrix
    created by the layer, and `bias` is a bias vector created by the layer
    (only applicable if `use_bias` is `True`).

    Note: if the input to the layer has a rank greater than 2, then
    it is flattened prior to the initial dot product with `kernel`.

    # Example

    ```python
        # as first layer in a sequential model:
        model = Sequential()
        model.add(Dense(32, input_shape=(16,)))
        # now the model will take as input arrays of shape (*, 16)
        # and output arrays of shape (*, 32)

        # after the first layer, you don't need to specify
        # the size of the input anymore:
        model.add(Dense(32))

註釋非常給力，densely-connected就是之前說是的全連接層

Q3:

model.summary()

可以顯示網絡的結構，很人性化有木有

_________________________________________________________________
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      
=================================================================