Это код, где я смешиваю тензорный поток с керасом.
def dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropout_rate=None, weight_decay=1e-4,
grow_nb_filters=True, return_concat_list=False):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
Args:
x: keras tensor
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
bottleneck: bottleneck block
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
return_concat_list: return the list of feature maps along with the actual output
Returns: keras tensor with nb_layers of conv_block appended
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x_list = [x]
for i in range(nb_layers):
cb = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
########################################################
#This is where I mix tensorflow with keras.
cb = K.reshape(cb,(-1,7*7*32))
W = weight_variable([7*7*32,7*7*32])
cb = tf.matmul(cb, W)
cb = K.reshape(cb,(-1,7,7,32))
x_list.append(cb)
######################################################
x = concatenate([x, cb], axis=concat_axis)
if grow_nb_filters:
nb_filter += growth_rate
if return_concat_list:
return x, nb_filter, x_list
else:
return x, nb_filter
, но получаю ошибку, подобную этой:
AttributeError: у объекта 'Tensor' нет атрибута '_keras_history'