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[英]TensorFlow - ValueError: Shapes (None, 1) and (None, 10) are incompatible
[英]Tensorflow error in Colab - ValueError: Shapes (None, 1) and (None, 10) are incompatible
我正在嘗試使用 MNIST 數據集為 NN 執行一個小代碼以進行字符識別。 當涉及到擬合線時,我得到 ValueError: Shapes (None, 1) and (None, 10) is incompatible
import numpy as np
#Install Tensor Flow
try:
#Tensorflow_version solo existe en Colab
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
tf.__version__
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape)
print(np.unique(y_train))
print(np.unique(y_test))
import matplotlib.pyplot as plt
plt.imshow(x_train[0], cmap='Greys');
y_train[0]
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train.shape
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(units=512, activation='relu'),
tf.keras.layers.Dense(units=10, activation='softmax')
])
model.summary()
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
h = model.fit(x_train, y_train, epochs=10, batch_size=256)
我在最后一行出現錯誤,例如 x_train 和 y_train 的大小是否不同。 但是 X_train 是 60000x28x28 而 y_train 是 60000x1
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 784) 0
_________________________________________________________________
dense (Dense) (None, 512) 401920
_________________________________________________________________
dense_1 (Dense) (None, 10) 5130
=================================================================
Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-10-50705bca2031> in <module>()
6 model.summary()
7 model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
----> 8 h = model.fit(x_train, y_train, epochs=10, batch_size=256)
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:533 train_step **
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:143 __call__
losses = self.call(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 1) and (None, 10) are incompatible
問題在這里:
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
損失, categorical_crossentropy
期望類的 one-hot 編碼向量,如此處所述。 但是,您的標簽不是一種熱編碼。 在這種情況下,最簡單的解決方案是使用loss='sparse_categorical_crossentropy'
因為您的標簽是稀疏的。
在將y_train
向量傳遞給fit
方法之前,您需要對其進行一次熱編碼。 您可以使用以下代碼執行此操作:
from keras.utils import to_categorical
# make the model and load the training dataset.
y_train = to_categorical(y_train)
# call the fit method.
https://stackoverflow.com/a/71385481/14997609這個對我有用。 只需更換
loss=tf.keras.losses.categorical_crossentropy
和;
loss=tf.keras.losses.sparse_categorical_crossentropy
我沒有嘗試過您的代碼,但通常這些錯誤來自不正確的索引。 我的意思是,你的最后一層不適合你的輸出或類似的東西。 我遇到了同樣的問題,我這樣解決了:
number_of_outputs = 10
# 10 is an example, you need to know how many outputs you have in your dataset
model.add(Dense(number_of_outputs, activation='softmax'))
一個例子是:
model = Sequential()
model.add(Dense(16, input_shape=(X.shape[1],), activation='relu'))
model.add(Dense(10, activation='softmax'))
# where 10 is my number of outputs in my dataset
model.summary()
我希望我解決了你的問題
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