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[英]Keras Value Error when checking input: expected dense_27_input to have 5 dimensions, but got array with shape (32, 150, 150, 3)
[英]Keras Train Neural Network Dimension Value Error: expected to have 2 dimensions, but got array with shape (32, 1, 4)
Python 3.6
Keras 2.2
Tensorflow 1.8 backend
我在訓練我的神經網絡時遇到了麻煩,因為出現了以下錯誤:
ValueError: Error when checking target: expected t_dense_3 to have 2 dimensions, but got array with shape (32, 1, 4)
我的神經網絡
>>> sgd = optimizers.SGD(lr=0.01, decay=1e-6)
>>> target_q_network = Sequential([
Dense(40, input_shape=observation_shape, activation='relu', name='t_dense_1'),
Dense(40, activation='relu', name='t_dense_2'),
Dense(number_of_actions, activation='linear', name='t_dense_3')
])
>>> target_q_network.compile(loss='mean_squared_error', optimizer=sgd)
>>> observation_shape
(8,)
-----------------------------------------------------------------
(Pdb) target_q_network.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
t_dense_1 (Dense) (None, 40) 360
_________________________________________________________________
t_dense_2 (Dense) (None, 40) 1640
_________________________________________________________________
t_dense_3 (Dense) (None, 4) 164
=================================================================
Total params: 2,164
Trainable params: 2,164
Non-trainable params: 0
_________________________________________________________________
當我將值傳遞給神經網絡時,將返回形狀(1、4)的數組:
(Pdb) env.reset()
array([-0.00126171, 0.94592496, -0.12780861, 0.35410735, 0.00146875, 0.02895054, 0. , 0. ])
# Passing value into Neural Network
(Pdb) target_q_network.predict(env.reset().reshape(1,8))
array([[ 0.07440183, 0.03480911, 0.11266299, -0.08043154]], dtype=float32)
我正在傳遞training_set
和labels
(Pdb) training_set.shape
(32, 8)
(Pdb) labels.shape
(32, 1, 4)
'mean_squared_error'
損失函數可能期望接收(batch_sz x n_labels)
標簽矩陣,但是您要傳遞(batch_sz x 1 x n_labels)
標簽矩陣,尤其是使用labels.shape=(32, 1, 4)
。 您只需要調整labels
的形狀以使其具有形狀(batch_sz x n_labels)
,使其具有labels.shape=(32, 4)
,然后可以將其與神經網絡輸出進行適當比較。
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