Question

我在使用Tensorboard时遇到问题，特别是在Hyperas模型中，histogram_freq不为零。

我只在tensorboard-callback中添加了Hyperas示例。如果histogram_freq = 0一切正常。但如果不同，我得到了错误：

InvalidArgumentError (see above for traceback): Shape [-1,784] has negative dimensions
 [[Node: dense_1_input = Placeholderdtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/cpu:0"]]

我正在使用：

Windows 7

tensorflow 1.3.0
tensorflow-tensorboard 0.1.6
hyperas 0.4
hyperopt 0.1
python 3.5.3

我试过不同的机器（Windows 10）和tensorflow-version 1.2.1。有谁知道如何解决它？

Example.py：

from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from keras.datasets import mnist
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential
from keras.utils import np_utils
import keras.callbacks as callbacks

from hyperas import optim
from hyperas.distributions import choice, uniform, conditional


def data():
"""`
Data providing function:

This function is separated from model() so that hyperopt
won't reload data for each evaluation run.
"""
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
nb_classes = 10
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
return x_train, y_train, x_test, y_test


def model(x_train, y_train, x_test, y_test):
"""
Model providing function:
Create Keras model with double curly brackets dropped-in as needed.
Return value has to be a valid python dictionary with two customary keys:
    - loss: Specify a numeric evaluation metric to be minimized
    - status: Just use STATUS_OK and see hyperopt documentation if not feasible
 The last one is optional, though recommended, namely:
    - model: specify the model just created so that we can later use it again.
 """
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([256, 512, 1024])}}))
model.add(Activation({{choice(['relu', 'sigmoid'])}}))
model.add(Dropout({{uniform(0, 1)}}))
# If we choose 'four', add an additional fourth layer
if conditional({{choice(['three', 'four'])}}) == 'four':
    model.add(Dense(100))

# We can also choose between complete sets of layers

model.add({{choice([Dropout(0.5), Activation('linear')])}})
model.add(Activation('relu'))

model.add(Dense(10))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
          optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})

tbCallBack = callbacks.TensorBoard(log_dir='./Graph', histogram_freq=1,
                        write_graph=True, write_images=True)

model.fit(x_train, y_train,
           batch_size={{choice([64, 128])}},
           epochs=3,
           verbose=2,
           validation_data=(x_test, y_test),
           callbacks=[tbCallBack])
score, acc = model.evaluate(x_test, y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}


if __name__ == '__main__':
best_run, best_model = optim.minimize(model=model,
                                  data=data,
                                  algo=tpe.suggest,
                                  max_evals=2,
                                  trials=Trials())
X_train, Y_train, X_test, Y_test = data()
print("Evalutation of best performing model:")
print(best_model.evaluate(X_test, Y_test))
print("Best performing model chosen hyper-parameters:")
print(best_run)

在Hyperas模型中使用Tensorboard（直方图）时出错

0 个答案: