使用tensorflow作为后端的keras：无法将feed_dict键解释为Tensor：无法将int转换为Tensor

我想在keras中使用tensorboard . 以下是我的代码：

from keras.layers import merge, Dropout, Convolution2D, MaxPooling2D, Input, Dense, Flatten, Merge
from keras.models import Model
from keras.callbacks import EarlyStopping, ReduceLROnPlateau,TensorBoard
import pickle
from sklearn.utils import shuffle
import numpy as np
import random
from keras.optimizers import Adam
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF


np.random.seed(1000) 

def load_pickled_data(file, columns):
    with open(file, mode='rb') as f:
        dataset = pickle.load(f)
    return tuple(map(lambda c: dataset[c], columns))

train_preprocessed_dataset_file = "train.p"
test_preprocessed_dataset_file = "test.p"

X_train, y_train_64 = load_pickled_data(train_preprocessed_dataset_file, columns = ['features', 'labels'])
X_test, y_test_64 = load_pickled_data(test_preprocessed_dataset_file, columns = ['features', 'labels'])

y_train = y_train_64.astype(np.float32)
y_test = y_test_64.astype(np.float32)

old_session = KTF.get_session()

with tf.Graph().as_default():
    session = tf.Session('')
    KTF.set_session(session)
    KTF.set_learning_phase(1)
    ###CNN model###
    input_img = Input(shape=(32, 32, 1))

    conv_1 = Convolution2D(32, 5, 5, border_mode='same',     activation='relu')(input_img)
    pool_1 = MaxPooling2D((2, 2))(conv_1)
    pool_1 = Dropout(0.1)(pool_1)

    conv_2 = Convolution2D(64, 5, 5, border_mode='same', activation='relu')(pool_1)
    pool_2 = MaxPooling2D((2, 2))(conv_2)
    pool_2 = Dropout(0.2)(pool_2)

    conv_3 = Convolution2D(128, 5, 5, border_mode='same', activation='relu')(pool_2)
    pool_3 = MaxPooling2D((2, 2))(conv_3)
    pool_3 = Dropout(0.3)(pool_3)
    pool_3 = Flatten()(pool_3)

    pool_1 = MaxPooling2D((4, 4))(pool_1)
    pool_1 = Flatten()(pool_1)

    pool_2 = MaxPooling2D((2, 2))(pool_2)
    pool_2 =Flatten()(pool_2)

    all_features =  merge([pool_1, pool_2, pool_3], mode='concat')

    logits = Dense(500,activation='relu')(all_features)
    logits = Dropout(0.5)(logits)
    res = Dense(43,activation='softmax')(logits)

    c_model = Model(input_img, res)
    c_model.summary()

    adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
    c_model.compile(loss='categorical_crossentropy', optimizer= adam, metrics=['accuracy'])
    tensor_board = TensorBoard(log_dir='./logs', histogram_freq=1)

    history = c_model.fit(X_train, y_train, batch_size=128,nb_epoch=3,shuffle=True,verbose=1,validation_split=0.25,callbacks=[tensor_board])

    loss_and_metrics = c_model.evaluate(X_test, y_test, batch_size=128)

KTF.set_session(old_session)

但错误发生如下：

文件“/home/jasontian/enter/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py”，第866行，在runfile execfile（文件名，命名空间）文件“/ home / jasontian / enter /lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py“，第102行，在execfile exec中（编译（f.read（），filename，'exec'），命名空间）文件”/ media /jasontian/keras_tf.py“，第111行，历史记录= c_model.fit（X_train，y_train，batch_size = 128，nb_epoch = 3，shuffle = True，verbose = 1，validation_split = 0.25，callbacks = [tensor_board]）文件” /home/jasontian/enter/lib/python3.5/site-packages/keras/engine/training.py“，第1196行，in fit initial_epoch = initial_epoch）文件”/home/jasontian/enter/lib/python3.5/ site-packages / keras / engine / training.py“，第911行，在_fit_loop callbacks.on_epoch_end（epoch，epoch_logs）文件”/home/jasontian/enter/lib/python3.5/site-packages/keras/callbacks.py “，第76行，在on_epoch_end callback.on_epoch_end（epoch，logs）文件”/home/jasontian/enter/lib/python3.5/site-packages/keras/callbacks.py“，第65行3，在on_epoch_end result = self.sess.run（[self.merged]，feed_dict = feed_dict）文件“/home/jasontian/enter/lib/python3.5/site-packages/tensorflow/python/client/session.py “，第766行，运行run_metadata_ptr）文件”/home/jasontian/enter/lib/python3.5/site-packages/tensorflow/python/client/session.py“，第921行，在_run e.args [0]中）TypeError：无法将feed_dict键解释为Tensor：无法将int转换为Tensor .

起初我以为它可能是 y_train.dtype （它是float64），但我发现它在一个例子中运行良好 . 更新：X_train的形状是（39209,32,32,1） . 那我怎么解决这个问题呢？