keras基于卷积网络手写数字识别

import time

import keras
from keras.utils import np_utils

start = time.time()
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
rows = 28
cols = 28
CLASSES = 10
x_train = x_train.reshape(x_train.shape[0], rows, cols, 1)
x_test = x_test.reshape(x_test.shape[0], rows, cols, 1)
y_train = np_utils.to_categorical(y_train, CLASSES)
y_test = np_utils.to_categorical(y_test, CLASSES)

x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255
x_test /= 255

model = keras.models.Sequential([
    keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=x_train.shape[1:]),
    keras.layers.MaxPool2D(pool_size=(2, 2)),
    keras.layers.Conv2D(32, (3, 3), activation='relu'),
    keras.layers.Conv2D(64, (3, 3), activation='relu'),
    keras.layers.Flatten(),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dropout(0.5),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dropout(0.5),
    keras.layers.Dense(10, activation='softmax')
])
model.summary()
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=5)
evaluate = model.evaluate(x_test, y_test)
print(evaluate)
print("elapsed: ", time.time() - start)
model.save("mnist-con.h5")
keras基于卷积网络手写数字识别

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