我正在使用单位标准网络，我可以轻松地用keras对其进行培训。但是，当我想使用model.predict时，会收到以下错误消息，InvalidArgument。我不知道为什么会这样。 训练网络时，我的输入形状为[H，W，3]，而执行模型时，输入形状相同。预测
有人可以帮忙吗？
我还列出了单位网络和训练方式。
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)
并按照以下方式进行培训：
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))
和模型。预测
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)]]
答案 0 :(得分：0)
仔细检查您要提供给模型的输入形状。
inputs = Input（（None，None，n_input_channels））
训练模型和预测时的输入形状应相同（H，W，通道）
InvalidArgumentError：ConcatOp：输入的尺寸应匹配： 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：根据需要更改变量。
答案 1 :(得分：0)
感谢@Saugat Bhattarai帮助我找到答案。我发现要进行预测，您的图像大小必须是2的幂。因此，H和W可以是8,32,64,128，....不需要为训练提供完全相同的图像大小和预测，只要大小为2的幂。因此，我尝试使用大小为（32,32,3）的训练数据图像并使用（128,128,3）进行预测的数据为例，它非常有效。 希望这对其他人有帮助。