kerasの基本的な使い方(3)--ニューラルネットワークを作成する
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作者:Tyan博客:noahsnail.com | CSDN |
本文は主にKerasのいくつかの基本的な使い方を紹介します. Demo 結果
本文は主にKerasのいくつかの基本的な使い方を紹介します.
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten
from keras.optimizers import Adam
#
(X_train, y_train), (X_test, y_test) = mnist.load_data()
#
X_train = X_train.reshape(-1, 1, 28, 28)
X_test = X_test.reshape(-1, 1, 28, 28)
# label
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)
#
model = Sequential()
#
model.add(Conv2D(32, kernel_size = (5, 5), strides = (1, 1), padding = 'same', activation = 'relu', input_shape = (1, 28, 28)))
#
model.add(MaxPooling2D(pool_size = (2, 2), strides = (1, 1), padding = 'same'))
#
model.add(Conv2D(64, kernel_size = (5, 5), strides = (1, 1), padding = 'same', activation = 'relu'))
#
model.add(MaxPooling2D(pool_size = (2, 2), strides = (1, 1), padding = '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)
# 、 、
model.compile(optimizer = adam, loss = 'categorical_crossentropy', metrics = ['accuracy'])
#
model.fit(X_train, y_train, epochs = 2, batch_size = 32)
#
loss, accuracy = model.evaluate(X_test, y_test)
print ''
print 'loss: ', loss
print 'accuracy: ', accuracy
Using TensorFlow backend.
Epoch 1/2
60000/60000 [==============================] - 55s - loss: 0.4141 - acc: 0.9234
Epoch 2/2
60000/60000 [==============================] - 56s - loss: 0.0743 - acc: 0.9770
9920/10000 [============================>.] - ETA: 0s
loss: 0.103529265788
accuracy: 0.9711