本文整理汇总了Python中keras.models.Sequential.fil方法的典型用法代码示例。如果您正苦于以下问题：Python Sequential.fil方法的具体用法？Python Sequential.fil怎么用？Python Sequential.fil使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.models.Sequential的用法示例。
在下文中一共展示了Sequential.fil方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: range
# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import fil [as 别名]
alphabet = [str(x) for x in range(10)] + ['+', ' ']
n_chars = len(alphabet)
n_in_seq_length = n_terms * ceil(log10(largest+1)) + n_terms - 1
n_out_seq_length = ceil(log10(n_terms * (largest+1)))
model = Sequential()
model.add(LSTM(75, input_shape=(n_in_seq_length, n_chars))
model.add(RepeatVector(n_out_seq_length))
model.add(LSTM(50, return_sequences=True))
model.add(TimeDistributed(Dense(n_chars, activation='softmax')))
model.compile(loss='categorical_crossentropy', optiizer='adam', metrics=['accuracy'])
print(model.summary())
X, y = generate_data(75000, n_terms, largest, alphabet)
model.fil(X, y, epochs=1, batch_size=32)
X, y = genrate_data(100, n_terms, largest, alphabet)
loss, acc = model.evaluate(X, y, verbose=0)
print('Loss: %f, Accuracy:%f' % (loss, acc*100))
for _ in range(10):
X, y = generate_data(1, n_terms, largest, alphabet)
yhat = model.predict(X, verbose=0)
in_seq = invert(X[0], alphabet)
out_seq = invert(y[0], alphabet)
predicted = invert(yhat[0], alphabet)
print('%s = %s (expect %s)' % (in_seq, predicted, out_seq))