python - Keras model.predict，InvalidArgumentError，ConcatOp : Dimensions of inputs should match

Question

我正在使用单位标准网络，我可以轻松地使用 keras 对其进行训练。但是当我想使用 model.predict 时，我收到以下错误消息:InvalidArgument。我不知道为什么会发生这种情况。 训练网络时我的输入形状为 [H,W,3]，并且在进行 model.predict 时输入形状相同

有人可以帮忙吗？

我也把我的单位网络和我训练的方式放上去。

def UNet(n_input_channels, n_output_channels):
from keras.layers import Input, Dropout, UpSampling2D, MaxPooling2D, BatchNormalization, Conv2D, Concatenate
from keras.models import Model

inputs = Input((None, None, n_input_channels))
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
conv1 = BatchNormalization()(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = BatchNormalization()(conv2)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
conv2 = BatchNormalization()(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = BatchNormalization()(conv3)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
conv3 = BatchNormalization()(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = BatchNormalization()(conv4)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
conv4 = BatchNormalization()(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = BatchNormalization()(conv5)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
conv5 = BatchNormalization()(conv5)
drop5 = Dropout(0.5)(conv5)

up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = Concatenate(axis=-1)([conv4,up6])
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = BatchNormalization()(conv6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
conv6 = BatchNormalization()(conv6)

up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = Concatenate(axis=-1)([conv3,up7])
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = BatchNormalization()(conv7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
conv7 = BatchNormalization()(conv7)

up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = Concatenate(axis=-1)([conv2,up8])
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = BatchNormalization()(conv8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
conv8 = BatchNormalization()(conv8)

up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = Concatenate(axis=-1)([conv1,up9])
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = BatchNormalization()(conv9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = BatchNormalization()(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = BatchNormalization()(conv9)
conv10 = Conv2D(n_output_channels, 1, activation = 'softmax')(conv9)

return Model(inputs = inputs, outputs = conv10)

并进行训练:

model_unet = UNet(n_input_channels=3, n_output_channels=3)
model_unet.compile(optimizer='adam', loss=dice_coef_loss, metrics=[dice_coef])
history_unet = model_unet.fit(x_train, y_train, batch_size=batchsize, epochs=epochs, 
                    verbose=1, shuffle=True, 
                    validation_data=(x_validation, y_validation))

和 model.predict

predictions = model.predict(sample_images, batch_size=4)

InvalidArgumentError

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
   2115             magic_arg_s = self.var_expand(line, stack_depth)
   2116             with self.builtin_trap:
-> 2117                 result = fn(magic_arg_s, cell)
   2118             return result
   2119 

<decorator-gen-60> in time(self, line, cell, local_ns)

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
    186     # but it's overkill for just that one bit of state.
    187     def magic_deco(arg):
--> 188         call = lambda f, *a, **k: f(*a, **k)
    189 
    190         if callable(arg):

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
   1191         else:
   1192             st = clock2()
-> 1193             exec(code, glob, local_ns)
   1194             end = clock2()
   1195             out = None

<timed exec> in <module>()

<ipython-input-39-6b97b7df800f> in predict(model, num_samples, indices)
     10     sample_masks = masks[samples_ind, :142, :92]
     11     print samplep_images.shape
---> 12     predictions = model.predict(sample_images, batch_size=4)
     13     toc = time.time()
     14     print('Time per image = {:.4f} sec'.format((toc-tic) / num_samples))

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/keras/engine/training.pyc in predict(self, x, batch_size, verbose, steps)
   1167                                             batch_size=batch_size,
   1168                                             verbose=verbose,
-> 1169                                             steps=steps)
   1170 
   1171     def train_on_batch(self, x, y,

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/keras/engine/training_arrays.pyc in predict_loop(model, f, ins, batch_size, verbose, steps)
    292                 ins_batch[i] = ins_batch[i].toarray()
    293 
--> 294             batch_outs = f(ins_batch)
    295             batch_outs = to_list(batch_outs)
    296             if batch_index == 0:

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in __call__(self, inputs)
   2713                 return self._legacy_call(inputs)
   2714 
-> 2715             return self._call(inputs)
   2716         else:
   2717             if py_any(is_tensor(x) for x in inputs):

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in _call(self, inputs)
   2673             fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
   2674         else:
-> 2675             fetched = self._callable_fn(*array_vals)
   2676         return fetched[:len(self.outputs)]
   2677 

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in __call__(self, *args, **kwargs)
   1380           ret = tf_session.TF_SessionRunCallable(
   1381               self._session._session, self._handle, args, status,
-> 1382               run_metadata_ptr)
   1383         if run_metadata:
   1384           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/work/banafsheh.beheshtipour/virenv/local/lib/python2.7/site-packages/tensorflow/python/framework/errors_impl.pyc in __exit__(self, type_arg, value_arg, traceback_arg)
    517             None, None,
    518             compat.as_text(c_api.TF_Message(self.status.status)),
--> 519             c_api.TF_GetCode(self.status.status))
    520     # Delete the underlying status object from memory otherwise it stays alive
    521     # as there is a reference to status from this from the traceback due to

InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,512,17,11] vs. shape[1] = [1,512,16,10]
     [[Node: concatenate_1/concat = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](batch_normalization_8/cond/Merge, conv2d_11/Relu, concatenate_1/concat-2-LayoutOptimizer)]]

Answer 1

仔细检查您想要为模型提供的输入形状。

inputs = Input((None, None, n_input_channels))

训练模型和预测时输入的形状应该相同。(H，W， channel )

InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,512,17,11] vs. shape[1] = [1,512,16,10]

更新: 将输入大小调整为 (32,32,3)

img = cv2.resize(img, (32, 32)) 

注意:根据您的需要更改变量。