Question

我正在尝试创建一个在其中使用某些Keras函数的Tensorflow模型。

该模型基于Keras MNIST CNN示例，它由一个简单的卷积层堆栈和一个Gaussian的估计组成。为了评估Gaussian函数，我使用Dense(2)估计其均值和标准差。然后，使用tf.range和tf.exp将这两个参数组合在一起，形成一个Gaussian函数。然后将此Gaussian张量与网络相乘，以产生最终输出。

如果我运行的模型没有Gaussian乘法，则一切正常。当我引入Gaussian位时，出现错误：

RuntimeError: Graph disconnected: cannot obtain value for tensor
  Tensor("Exp_2:0", shape=(?, 9216), dtype=float32) at layer "tf". The
  following previous layers were accessed without issue: ['input_2',
  'conv2d_3', 'conv2d_4', 'max_pooling2d_2', 'dropout_3', 'flatten_2']

这是一个完全可复制的脚本：

'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Model, Input
from keras.layers import Dense, Dropout, Flatten, Multiply
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

inputs = Input(shape=input_shape)
net = Conv2D(32, kernel_size=(3, 3),
                 activation='relu')(inputs)
net = Conv2D(64, (3, 3), activation='relu')(net)
net = MaxPooling2D(pool_size=(2, 2))(net)
net = Dropout(0.25)(net)
net = Flatten()(net)

gauss_par = Dense(2, activation='relu')(net)
# Define a vector from 1 to n_frames
x1d = K.tf.range(0, 9216, dtype='float32')
# Transform the vector into a tensor of shape [batch_size, n_frames]
x = K.tf.tile(K.tf.reshape(x1d, [1, -1]), [K.tf.shape(inputs)[0], 1])
# Gaussian function
gaussian = K.tf.exp(-K.tf.pow(x - gauss_par[:, 0], 2) / 2 / K.tf.pow(gauss_par[:, 1], 2))
# Transform the tensorflow tensor into a keras tensor
gaussian_layer = Input(tensor=gaussian, name='tf')

net = Multiply()([net, gaussian_layer])
net = Dense(128, activation='relu')(net)
net = Dropout(0.5)(net)
outputs = Dense(num_classes, activation='softmax')(net)

model = Model(inputs=inputs, outputs=outputs)
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.summary()
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

您知道什么可能导致此错误吗？

使用Tensorflow张量时Keras和图形断开连接的问题

0 个答案: