Question

下面显示的代码给出了我的值错误

model = Sequential()
model.add(Conv2D(32, (8,8),
                 padding='valid',
                 strides=1,
                 activation="relu", input_shape = (256,256,3)))

我得到的错误是

ValueError: `Layer conv2d_37 was called with an input that isn't a symbolic tensor.` Received type: <class 'tensorflow.python.framework.ops.Tensor'>. Full input: [<tf.Tensor 'conv2d_37_input:0' shape=(?, 256, 256, 3) dtype=float32>]. All inputs to the layer should be tensors.

我在这里使用Tensorflow 1.2.1版本和keras 2.1.5，在运行main时，我在这里收到此错误。

伙计们，请帮助解决这个问题。

完整的代码在

下面

def cnn_model(X_train, y_train, kernel_size, nb_filters, channels, nb_epoch, batch_size, nb_classes, nb_gpus):

model = Sequential()

model.add(Conv2D(nb_filters, (kernel_size[0], kernel_size[1]),
                 padding='valid',
                 strides=1,
                 input_shape=(img_rows, img_cols, channels), activation="relu"))

model.add(Conv2D(nb_filters, (kernel_size[0], kernel_size[1]), activation="relu"))

model.add(Conv2D(nb_filters, (kernel_size[0], kernel_size[1]), activation="relu"))

model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
print("Model flattened out to: ", model.output_shape)

model.add(Dense(128))
model.add(Activation('sigmoid'))
model.add(Dropout(0.25))

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

model = multi_gpu_model(model, gpus=nb_gpus)

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

stop = EarlyStopping(monitor='val_acc',
                     min_delta=0.001,
                     patience=2,
                     verbose=0,
                     mode='auto')

tensor_board = TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)

model.fit(X_train, y_train, batch_size=batch_size, epochs=nb_epoch,
          verbose=1,
          validation_split=0.2,
          class_weight='auto',
          callbacks=[stop, tensor_board])

return model

谢谢

使用不是符号张量的输入调用层conv2d_37

0 个答案: