python - 我得到一个没有意义的尺寸错误“预期形状=”

Question

所以我对unet模型做了如下实现：

def unet_model(optimizer, loss_metric, metrics, sample_width, sample_height, lr=1e-3):
    inputs = Input((sample_width, sample_height, 1))
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    drop1 = Dropout(0.5)(pool1)

    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(drop1)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    drop2 = Dropout(0.5)(pool2)

    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(drop2)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    drop3 = Dropout(0.3)(pool3)

    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(drop3)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
    drop4 = Dropout(0.3)(pool4)

    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(drop4)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)

    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)

    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)

    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)

    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

    conv10 = Conv2D(1, (1, 1), activation='softmax')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])

    model.compile(optimizer=optimizer(lr=lr), loss=loss_metric, metrics=metrics)
    return model

我还有一个数据加载器来加载 (1024, 1024,1) 形式的图像，如下所示：

# Load datasets
train_input, train_output = dataloader(filepath, "train")
test_input, test_output = dataloader(filepath, "test")
print(train_input.shape)
print(train_output.shape)
print(test_input.shape)
print(test_output.shape)

# Load model
model = unet_model(optimizer=Adam, loss_metric=dice_coef_loss, metrics=[dice_coef], sample_width=train_input.shape[0], sample_height=train_input.shape[1],lr=1e-3)
model.compile(optimizer=Adam(lr=0.01), loss=dice_coef_loss, metrics=[])
history = model.fit(x=train_input, y=train_output, epochs=30)

但是当我运行它时，我得到以下输出：

(1024, 1024, 129)
(1024, 1024, 129)
(1024, 1024, 18)
(1024, 1024, 18)
Epoch 1/30
Traceback (most recent call last):
  File "/usr/lib/python3.8/contextlib.py", line 131, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/ops/variable_scope.py", line 2825, in variable_creator_scope
    yield
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1100, in fit
    tmp_logs = self.train_function(iterator)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 725, in _initialize
    self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3196, in _create_graph_function
    func_graph_module.func_graph_from_py_func(
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 977, in wrapper
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
        outputs = model.train_step(data)
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:754 train_step
        y_pred = self(x, training=True)
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:998 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    /home/tzikos/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/input_spec.py:271 assert_input_compatibility
        raise ValueError('Input ' + str(input_index) +
    ValueError: Input 0 is incompatible with layer model: expected shape=(None, 1024, 1024, 1), found shape=(32, 1024, 129)

所以它进入了训练时期，但无法通过它，并且由于某种原因，它在网络中固有地失去了它的一个维度，我不知道在哪里或如何。谁能帮我？

标签： pythontensorflowmachine-learningdeep-learningtensorflow2.0