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[英]Tensorflow error in Colab - ValueError: Shapes (None, 1) and (None, 10) are incompatible
[英]TensorFlow - ValueError: Shapes (None, 1) and (None, 10) are incompatible
我正在嘗試使用此鏈接中的“街景門牌號 (SVHN) 數據集”來實現圖像分類器。 我正在使用格式 2,其中包含從 0 到 9 的 32x32 RGB 居中數字圖像。當我嘗試編譯和擬合 model 時,出現以下錯誤:
Epoch 1/10
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-31870b6986af> in <module>()
3
4 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
----> 5 model.fit(trainX, trainY, validation_data=(validX, validY), batch_size=128, epochs=10)
9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:756 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:203 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:152 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:256 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:1537 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:4833 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 1) and (None, 10) are incompatible
代碼是:
model = Sequential([
Conv2D(filters=64, kernel_size=3, strides=2, activation='relu', input_shape=(32,32,3)),
MaxPooling2D(pool_size=(2, 2), strides=1, padding='same'),
Conv2D(filters=32, kernel_size=3, strides=1, activation='relu'),
MaxPooling2D(pool_size=(2, 2), strides=1, padding='same'),
Flatten(),
Dense(10, activation='softmax')
])
model.summary()
Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_23 (Conv2D) (None, 15, 15, 64) 1792
_________________________________________________________________
max_pooling2d_23 (MaxPooling (None, 15, 15, 64) 0
_________________________________________________________________
conv2d_24 (Conv2D) (None, 13, 13, 32) 18464
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 13, 13, 32) 0
_________________________________________________________________
flatten_10 (Flatten) (None, 5408) 0
_________________________________________________________________
dense_13 (Dense) (None, 10) 54090
=================================================================
Total params: 74,346
Trainable params: 74,346
Non-trainable params: 0
_________________________________________________________________
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(trainX, trainY, validation_data=(validX, validY), batch_size=128, epochs=10)
我無法解決錯誤,有人對如何解決它有任何想法嗎?
因為我看不到你的 trainY 編碼; 好像 - 你的 trainY 只有一列,你的 model output 有 10 個神經元,所以 Shapes (None, 1) 和 (None, 10) 是不兼容的。 你可以在你的 trainY 上試試這個(即 one-hot 編碼)
from sklearn.preprocessing import LabelBinarizer
label_as_binary = LabelBinarizer()
train__y_labels = label_as_binary.fit_transform(trainY)
編譯看起來像(尋找train__y_labels)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_X_input, train__y_labels, batch_size=128, epochs=1)
注意:如果您的 valid 也引發錯誤,則所有 y(s) 都需要相同。
更改編譯語句,以便
loss = 'sparse_categorical_cross_entropy'
“稀疏”表示 y 值是數字而不是 one-hot
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