keras实现RNN手写字识别

红太狼 2022-12-15 03:37 156阅读 0赞

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)