莫烦keras学习自修第五天【CNN卷积神经网络】

1.代码实战

#!/usr/bin/env python
#! _*_ coding:UTF-8 _*_

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 Dense, Activation, Convolution2D, MaxPooling2D, Flatten
from keras.optimizers import Adam

# 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, 1,28, 28)/255.
X_test = X_test.reshape(-1, 1,28, 28)/255.
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# Another way to build your CNN
model = Sequential()

# Conv layer 1 output shape (32, 28, 28)
model.add(Convolution2D(
    batch_input_shape=(None, 1, 28, 28),
    filters=32,
    kernel_size=5,
    strides=1,
    padding='same',     # Padding method
    data_format='channels_first',
))
model.add(Activation('relu'))

# Pooling layer 1 (max pooling) output shape (32, 14, 14)
model.add(MaxPooling2D(
    pool_size=2,
    strides=2,
    padding='same',    # Padding method
    data_format='channels_first',
))

# Conv layer 2 output shape (64, 14, 14)
model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first'))
model.add(Activation('relu'))

# Pooling layer 2 (max pooling) output shape (64, 7, 7)
model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))

# Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024)
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))

# Fully connected layer 2 to shape (10) for 10 classes
model.add(Dense(10))
model.add(Activation('softmax'))

# Another way to define your optimizer
adam = Adam(lr=1e-4)

# We add metrics to get more results you want to see
model.compile(optimizer=adam,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=1, batch_size=64,)

print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)

print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)