python 小例子 源码 莫凡

import numpy as np

np.random.seed(1337) # for reproducibility

dels import Sequential

from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten

import matplotlib.pyplot as plt

from keras.utils import np_utils

from keras.optimizers import Adam

from keras.datasets import mnist

from keras import backend

backend.set_image_data_format('channels_first')

# %% 数据预处理

(X_train, Y_train), (X_test, Y_test) = mnist.load_data()

X_train = shape(-1, 1, 28 , 28)/255.

X_test = shape(-1, 1,28, 28)/255.

Y_train = _categorical(Y_train, num_classes=10)

Y_test = _categorical(Y_test, num_classes=10)

# %% 建立模型

model = Sequential()

model.add(

Convolution2D(

batch_input_shape=(None, 1, 28, 28),

filters=32,

kernel_size=5,

strides=1,

padding='same'

)

)

model.add(Activation('relu'))

model.add(

MaxPooling2D(

pool_size=2,

strides=2,

padding='same',

)

)

model.add(Activation('relu'))

model.add(Convolution2D(64, 5, strides=1, padding='same'))

model.add(Activation('relu'))

model.add(MaxPooling2D(2,2,'same'))

model.add(Flatten())

model.add(Dense(1024))

model.add(Activation('relu'))

model.add(Dense(10))

model.add(Activation('softmax'))

adam = Adam(lr=1e-4)

modelpile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])

# %% 训练

print('Training ------------')

model.fit(X_train,Y_train,batch_size=64)

print('nTesting ------------')

loss, accuracy = model.evaluate(X_test, Y_test)

# %% 评估

print('ntest loss: ', loss)

print('ntest accuracy: ', accuracy)

```

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