ValueError：尺寸必须相等，输入形状

时间：2021-02-09 12:31:11

标签： python python-3.x django tensorflow keras

我写了这段代码。我的输入形状是 (100 x100 X3)。我是深度学习的新手。 我在这上面花了很多时间，但无法解决这个问题。非常感谢任何帮助。

init = tf.random_normal_initializer(mean=0.0, stddev=0.05, seed=None)
input_image=Input(shape=image_shape)


# input: 100x100 images with 3 channels -> (3, 100, 100) tensors.
# this applies 32 convolution filters of size 3x3 each.
model=Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3),kernel_initializer=init,
                        padding='same', input_shape=(3,100,100)))
model.add(Activation('relu'))
model.add(Conv2D(filters=32,kernel_size=(3, 3),padding="same"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding="same"))
model.add(Dropout(0.25))

model.add(Conv2D(filters=32,kernel_size=(3, 3),padding="same"))
model.add(Activation('relu'))
model.add(Conv2D(filters=32, kernel_size=(3, 3),padding="same"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding="same"))
model.add(Dropout(0.25))

model.add(Flatten())
# Note: Keras does automatic shape inference.
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(10))
model.add(Activation('softmax'))
model.summary()
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
len(model.weights)
model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])

错误： 在 [15]: runfile('/user/Project/SM/src/ann_algo_keras.py', wdir='/user/Project/SM/src') 随机起始突触权重： 模型：“sequential_3”

层（类型）输出形状参数#

conv2d_12 (Conv2D)（无、3、100、16）14416

activation_18（激活）（无、3、100、16）0

conv2d_13 (Conv2D)（无、3、100、32）4640

activation_19（激活）（无、3、100、32）0

max_pooling2d_6 (MaxPooling2 (None, 2, 50, 32) 0

dropout_9 (Dropout) (None, 2, 50, 32) 0

conv2d_14 (Conv2D)（无、2、50、32）9248

activation_20（激活）（无、2、50、32）0

conv2d_15 (Conv2D)（无、2、50、32）9248

activation_21（激活）（无、2、50、32）0

max_pooling2d_7 (MaxPooling2 (None, 1, 25, 32) 0

dropout_10 (Dropout) (None, 1, 25, 32) 0

flatten_3 (Flatten) (None, 800) 0

dense_6（密集）（无，256）205056

activation_22（激活）（无，256）0

dropout_11（辍学）（无，256）0

dense_7（密集）（无，10）2570

activation_23（激活）（无，10）0

总参数：245,178 可训练参数：245,178 不可训练的参数：0

纪元 1/2000 回溯（最近一次调用）：

文件“/user/Project/SM/src/ann_algo_keras.py”，第 272 行，在 训练（输入，输出，图像形状）

文件“/user/Project/SM/src/ann_algo_keras.py”，第 204 行，训练中 model.fit(X_train, y_train, batch_size, epochs, validation_data=(X_test, y_test), use_multiprocessing=True)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py”，第108行，_method_wrapper 返回方法(self, *args, **kwargs)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py”，第1098行，合适 tmp_logs = train_function(iterator)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py”，第 780 行，调用 结果 = self._call(*args, **kwds)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py”，第 823 行，在 _call self._initialize(args, kwds, add_initializers_to=initializers)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py”，第 696 行，在 _initialize self._stateful_fn._get_concrete_function_internal_garbage_collected(#pylint: disable=protected-access

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py”，第 2855 行，在 _get_concrete_function_internal_garbage_collected graph_function, _, _ = self._maybe_define_function(args, kwargs)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py”，第 3213 行，在 _maybe_define_function graph_function = self._create_graph_function(args, kwargs)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py”，第 3065 行，在 _create_graph_function 中 func_graph_module.func_graph_from_py_func(

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py”，第 986 行，在 func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py”，第600行，在wrapped_fn return weak_wrapped_fn().wrapped(*args, **kwds)

文件“/home/user/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py”，第 973 行，在包装器中 引发 e.ag_error_metadata.to_exception(e)

值错误：在用户代码中：

/home/user/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
    return step_function(self, iterator)
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/home/catherin/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/home/catherin/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
    return fn(*args, **kwargs)
/home/catherin/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
    outputs = model.train_step(data)
/home/catherin/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:748 train_step
    loss = self.compiled_loss(
/home/catherin/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
    loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/home/catherin/.local/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:149 __call__
    losses = ag_call(y_true, y_pred)
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:253 call  **
    return ag_fn(y_true, y_pred, **self._fn_kwargs)
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:1195 mean_squared_error
    return K.mean(math_ops.squared_difference(y_pred, y_true), axis=-1)
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/ops/gen_math_ops.py:10398 squared_difference
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:742 _apply_op_helper
    op = g._create_op_internal(op_type_name, inputs, dtypes=None,
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:591 _create_op_internal
    return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3477 _create_op_internal
    ret = Operation(
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1974 __init__
    self._c_op = _create_c_op(self._graph, node_def, inputs,
/home/user/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1815 _create_c_op
    raise ValueError(str(e))

ValueError: Dimensions must be equal, but are 10 and 10000 for '{{node mean_squared_error/SquaredDifference}} = SquaredDifference[T=DT_FLOAT](sequential_3/activation_23/Softmax, IteratorGetNext:1)' with input shapes: [?,10], [?,1,10000].

2 个答案:

答案 0 :(得分：1)

只是与输入形状中通道的位置混淆。在 Keras 中，输入形状应为 =IFNA(INDEX(FILTER(C$3:C, D$3:D<G3, B$3:B>H3), 1)) 而不是 PyTorch 中的 HxWxC。

CxHxW

答案 1 :(得分：1)

您的输入顺序不正确，频道应该是最后。所以，

-infinity

另外，我假设您正在尝试进行分类。还有一些指标用于回归，例如“mae”。您可以将它们更改为：

10
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