Question

当我运行以下代码时：

from keras.models import Sequential
from keras.layers import Dense
import numpy
import time

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset

dataset = numpy.loadtxt("C:/Users/AQader/Desktop/Keraslearn/mammm.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:5]
Y = dataset[:,5]

# create model
model = Sequential()
model.add(Dense(50, input_dim=5, init='uniform', activation='relu'))
model.add(Dense(25, init='uniform', activation='tanh'))
model.add(Dense(15, init='uniform', activation='tanh'))
model.add(Dense(1, init='uniform', activation='sigmoid'))

# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Fit the model
model.fit(X, Y, nb_epoch=200, batch_size=20, verbose = 0)
time.sleep(0.1)

# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

我最终收到以下内容。

32/829 [>.............................] - ETA: 0sacc: 84.20%

就是这样。只有一条线在训练半分钟后出现。在查看其他问题后，通常的输出如下：

Epoch 1/20
1213/1213 [==============================] - 0s - loss: 0.1760     
Epoch 2/20
1213/1213 [==============================] - 0s - loss: 0.1840     
Epoch 3/20
1213/1213 [==============================] - 0s - loss: 0.1816     
Epoch 4/20
1213/1213 [==============================] - 0s - loss: 0.1915     
Epoch 5/20
1213/1213 [==============================] - 0s - loss: 0.1928     
Epoch 6/20
1213/1213 [==============================] - 0s - loss: 0.1964     
Epoch 7/20
1213/1213 [==============================] - 0s - loss: 0.1948     
Epoch 8/20
1213/1213 [==============================] - 0s - loss: 0.1971     
Epoch 9/20
1213/1213 [==============================] - 0s - loss: 0.1899     
Epoch 10/20
1213/1213 [==============================] - 0s - loss: 0.1957

谁能告诉我这里可能有什么问题？我是初学者但这似乎不正常。请注意，“代码”部分中没有错误。我的意思是0sacc出现了什么。我在Windows 7 64位计算机上的Anaconda Environment Python 2.7中运行它。 8GB RAM和酷睿i5第五代。

Answer 1

通过model.fit调用verbose = 0，您已经抑制了详细输出。尝试设置verbose = 1。

Keras输出看起来与其他输出不同

1 个答案: