Keras_morvan(四) ：RNN_classifier

首先对上一篇CNN博文各个层输出的shape值进行补充说明。X_train(-1, 1, 28, 28), -1是表示有多少张图片；1是channel，这里是黑白图片；28x28，每张图片大小。在经过conv1后输出的shape(32, 28, 28) 首先不管图片的张数，32表示channel，图片大小还是不变，卷积是不会改变图片太小，只是将图片高度增加。pooling之后shape（32, 14, 14）channel不变，图片大小变小了。整个过程就是将图片将高，压缩的更小。如图，channel就是k值变大，width,height变小。

**RNN_classifier**Recurrent Neural Network循环神经网络
首先了解一下啥是RNN， LSTM长短时间记忆。
a.首先添加RNN层，输入为训练数据，输出数据大小由CELL_SIZE定义。
b.然后添加输出层，激励函数选择softmax
c.训练，评估。

import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import SimpleRNN, Activation, Dense
from keras.optimizers import Adam

TIME_STEPS = 28     # same as the height of the image
INPUT_SIZE = 28     # same as the width of the image
BATCH_SIZE = 50
BATCH_INDEX = 0
OUTPUT_SIZE = 10
CELL_SIZE = 50
LR = 0.001


# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
X_train = X_train.reshape(-1, 28, 28) / 255.      # normalize
X_test = X_test.reshape(-1, 28, 28) / 255.        # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# build RNN model
model = Sequential()

# RNN cell
model.add(SimpleRNN(
    # for batch_input_shape, if using tensorflow as the backend, we have to put None for the batch_size.
    # Otherwise, model.evaluate() will get error.
    batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
    output_dim=CELL_SIZE,
    unroll=True,
))

# output layer
model.add(Dense(OUTPUT_SIZE))
model.add(Activation('softmax'))

# optimizer
adam = Adam(LR)
model.compile(optimizer=adam,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# training
for step in range(4001):
    # data shape = (batch_num, steps, inputs/outputs)
    X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
    Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]
    cost = model.train_on_batch(X_batch, Y_batch)
    BATCH_INDEX += BATCH_SIZE
    BATCH_INDEX = 0 if BATCH_INDEX >= X_train.shape[0] else BATCH_INDEX

    if step % 500 == 0:
        cost, accuracy = model.evaluate(X_test, y_test, batch_size=y_test.shape[0], verbose=False)
        print('test cost: ', cost, 'test accuracy: ', accuracy)

贴出原址，

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