使用Keras并在运行model.fit时出错，这会导致值错误。但是不完全确定在使用fit时检查模型是如何失败的。
ValueError: Error when checking model input: expected input_node to have 4 dimensions, but got array with shape (0, 1)
以下是堆栈跟踪的更多详细信息：
history = st_model.fit(X, y, batch_size=args.batch_size, nb_epoch=args.nb_epoch, verbose=2, validation_data=(X_cv, y_cv), initial_epoch=0)
File "/usr/local/lib/python2.7/site-packages/keras/engine/training.py", line 1116, in fit
batch_size=batch_size)
File "/usr/local/lib/python2.7/site-packages/keras/engine/training.py", line 1029, in _standardize_user_data
exception_prefix='model input')
File "/usr/local/lib/python2.7/site-packages/keras/engine/training.py", line 112, in standardize_input_data
str(array.shape))
此外，还有一些关于模型的更多背景信息。
st_model = st_convt(input_shape, mode=2, nb_res_layer=args.nb_res_layer)
history = st_model.fit(X, y, batch_size=args.batch_size, nb_epoch=args.nb_epoch, verbose=2, validation_data=(X_cv, y_cv), initial_epoch=0)
然后..
# inputs th ordering, BGR
def st_convt(input_shape, weights_path=None, mode=0, nb_res_layer=5):
if K.image_dim_ordering() == 'tf':
channel_axis = 3
else:
channel_axis = 1
input = Input(shape=input_shape, name='input_node', dtype=K.floatx())
# Downsampling
c11 = Convolution2D(32, 9, 9, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(input)
bn11 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c11)
a11 = Activation('relu')(bn11)
c12 = Convolution2D(64, 3, 3, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(2, 2), border_mode='same', activation='linear')(a11)
bn12 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c12)
a12 = Activation('relu')(bn12)
c13 = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(2, 2), border_mode='same', activation='linear')(a12)
bn13 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c13)
last_out = Activation('relu')(bn13)
for i in range(nb_res_layer):
c = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(last_out)
bn = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c)
a = Activation('relu')(bn)
c = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(a)
bn = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c)
# a = Activation('relu')(bn)
last_out = merge([last_out, bn], mode='sum')
# last_out = a
ct71 = ConvolutionTranspose2D(64, 3, 3, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(2, 2), border_mode='same', activation='linear')(last_out)
bn71 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(ct71)
a71 = Activation('relu')(bn71)
ct81 = ConvolutionTranspose2D(32, 3, 3, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(2, 2), border_mode='same', activation='linear')(a71)
bn81 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(ct81)
a81 = Activation('relu')(bn81)
c91 = Convolution2D(3, 9, 9, dim_ordering=K.image_dim_ordering(),
init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(a81)
out = ScaledSigmoid(scaling=255., name="output_node")(c91)
model = Model(input=[input], output=[out])
if weights_path:
model.load_weights(weights_path)
return model