莫烦Python 4

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RNN Classifier 循环神经网络

问题描述

使用RNN对MNIST里面的图片进行分类

关键

SimpleRNN()参数

batch_input_shape

使用状态RNN的注意事项

可以将RNN设置为‘stateful’，意味着由每个batch计算出的状态都会被重用于初始化下一个batch的初始状态。状态RNN假设连续的两个batch之中，相同下标的元素有一一映射关系。

要启用状态RNN，请在实例化层对象时指定参数stateful=True，并在Sequential模型使用固定大小的batch：通过在模型的第一层传入batch_size=(…)和input_shape来实现。在函数式模型中，对所有的输入都要指定相同的batch_size。

如果要将循环层的状态重置，请调用.reset_states()，对模型调用将重置模型中所有状态RNN的状态。对单个层调用则只重置该层的状态。

（samples，timesteps，input_dim）

代码

'''

RNN Classifier 循环神经网络

'''

import numpy as np

np.random.seed(1337)

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_step = 28

input_size = 28

batch_size = 50

output_size = 10

cell_size = 50

LR = 0.001

(X_train, y_train), (X_test, y_test) = mnist.load_data()

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)

model = Sequential()

model.add(

    SimpleRNN(

        batch_input_shape=(None, time_step, input_size),

        units=cell_size

    )

)

model.add(

    Dense(output_size)

)

model.add(Activation('softmax'))

adam = Adam(LR)

model.compile(

    optimizer=adam,

    loss='categorical_crossentropy',

    metrics=['accuracy']

)

model.summary()

model.fit(X_train, y_train, batch_size=batch_size, epochs=2, verbose=2, validation_data=(X_test, y_test))

结果

Model: "sequential_2"

_________________________________________________________________

Layer (type)                 Output Shape              Param #

=================================================================

simple_rnn_2 (SimpleRNN)     (None, 50)                3950

_________________________________________________________________

dense_2 (Dense)              (None, 10)                510

_________________________________________________________________

activation_2 (Activation)    (None, 10)                0

=================================================================

Total params: 4,460

Trainable params: 4,460

Non-trainable params: 0

_________________________________________________________________

Train on 60000 samples, validate on 10000 samples

Epoch 1/2

 - 12s - loss: 0.6643 - accuracy: 0.7966 - val_loss: 0.4501 - val_accuracy: 0.8550

Epoch 2/2

 - 9s - loss: 0.3220 - accuracy: 0.9087 - val_loss: 0.2445 - val_accuracy: 0.9359

巴特西

莫烦Python 4

莫烦Python 4

RNN Classifier 循环神经网络

问题描述

关键

SimpleRNN()参数

代码

结果

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