拟合 keras 模型时出现 ValueError答案

【问题标题】：ValueError when fitting keras model拟合 keras 模型时出现 ValueError
【发布时间】：2023-05-26 21:12:02
【问题描述】：

from sklearn.datasets import fetch_openml
import numpy as np
import keras

mnist = fetch_openml('mnist_784', version=1)   
X, y = mnist["data"], mnist["target"]

y = y.astype(np.uint8)

X_digits = [np.array(X.iloc[i]) for i in range(len(X))]
X = np.array([some_digit.reshape(28, 28) for some_digit in X_digits])

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

model = keras.models.Sequential([
    keras.layers.Conv2D(64, 7, activation="relu", padding="same",
                        input_shape=[28, 28, 1]),
    keras.layers.MaxPooling2D(2),
    keras.layers.Conv2D(128, 3, activation="relu", padding="same"),
    keras.layers.Conv2D(128, 3, activation="relu", padding="same"),
    keras.layers.MaxPooling2D(2),
    keras.layers.Conv2D(256, 3, activation="relu", padding="same"),
    keras.layers.Conv2D(256, 3, activation="relu", padding="same"),
    keras.layers.MaxPooling2D(2),
    keras.layers.Flatten(),
    keras.layers.Dense(128, activation="relu"),
    keras.layers.Dropout(0.5),
    keras.layers.Dense(64, activation="relu"),
    keras.layers.Dropout(0.5),
    keras.layers.Dense(10, activation="softmax")
])

model.compile(loss="categorical_crossentropy")

这一切似乎都很好。但是在这一行：

model.fit(X_train, y_train)

我收到此错误：

ValueError                                Traceback (most recent call last)
<ipython-input-19-d768f88d541e> in <module>()
----> 1 model.fit(X_train, y_train)

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1127           except Exception as e:  # pylint:disable=broad-except
   1128             if hasattr(e, "ag_error_metadata"):
-> 1129               raise e.ag_error_metadata.to_exception(e)
   1130             else:
   1131               raise

ValueError: in user code:

    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 878, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 867, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 810, in train_step
        y, y_pred, sample_weight, regularization_losses=self.losses)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
        losses = call_fn(y_true, y_pred)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1665, in categorical_crossentropy
        y_true, y_pred, from_logits=from_logits, axis=axis)
    File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 4994, in categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)

    ValueError: Shapes (32, 1) and (32, 10) are incompatible

这里出了什么问题？

【问题讨论】：

典型错误，您没有对标签进行一次性编码。
尝试使用“sparse_categorical_crossentropy”而不是“categorical_crossentropy”作为损失（这里的解释：*.com/a/62286888/10375049）

标签： keras conv-neural-network mnist

【解决方案1】：

作为@Dr.史努比你的标签形状不正确。将数据拆分为训练和测试后，应确保您的标签已使用您想要的类数（在本例中为 10）进行了普遍编码。只需将其放在拆分后即可：

from tensorflow.keras.utils import to_categorical
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
y_train.shape

输出应该是：

(60000, 10)

【讨论】：