TensorFlowでkeras
1407 ワード
概要
TnsorFlowでkerasやってみた。
sinを学習してみた。
写真
環境
windows 7 sp1 64bit
anaconda3
tensorflow 1.2
サンプルコード
import numpy as np
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers.core import Dense, Activation
from tensorflow.contrib.keras.python.keras.optimizers import SGD
import matplotlib.pyplot as plt
x = np.arange(200).reshape(-1, 1) / 30
y = np.sin(x)
model = Sequential()
model.add(Dense(30, input_shape = (1, )))
model.add(Activation('sigmoid'))
model.add(Dense(10))
model.add(Activation('sigmoid'))
model.add(Dense(1))
sgd = SGD(lr = 0.1)
model.compile(loss = 'mean_squared_error', optimizer = sgd)
model.summary();
model.fit(x, y, epochs = 400, batch_size = 10, verbose = 0)
predictions = model.predict(x)
print (np.mean(np.square(predictions - y)))
preds = model.predict(x)
plt.plot(x, y, 'b', x, preds, 'r--')
plt.savefig("keras10.png")
plt.show()
import numpy as np
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers.core import Dense, Activation
from tensorflow.contrib.keras.python.keras.optimizers import SGD
import matplotlib.pyplot as plt
x = np.arange(200).reshape(-1, 1) / 30
y = np.sin(x)
model = Sequential()
model.add(Dense(30, input_shape = (1, )))
model.add(Activation('sigmoid'))
model.add(Dense(10))
model.add(Activation('sigmoid'))
model.add(Dense(1))
sgd = SGD(lr = 0.1)
model.compile(loss = 'mean_squared_error', optimizer = sgd)
model.summary();
model.fit(x, y, epochs = 400, batch_size = 10, verbose = 0)
predictions = model.predict(x)
print (np.mean(np.square(predictions - y)))
preds = model.predict(x)
plt.plot(x, y, 'b', x, preds, 'r--')
plt.savefig("keras10.png")
plt.show()
Author And Source
この問題について(TensorFlowでkeras), 我々は、より多くの情報をここで見つけました https://qiita.com/ohisama@github/items/8a04cb6b01838a750c3b著者帰属:元の著者の情報は、元のURLに含まれています。著作権は原作者に属する。
Content is automatically searched and collected through network algorithms . If there is a violation . Please contact us . We will adjust (correct author information ,or delete content ) as soon as possible .