形状 (335476,50) 和 (3,50) 未对齐：50 (dim 1) != 3 (dim 0)

Question

我的RNN如下图

length_of_sequence = 3
in_out_neurons = 50
n_hidden = 128
model = Sequential()
model.add(LSTM(n_hidden, batch_input_shape=(None, length_of_sequence,in_out_neurons), return_sequences=True))
model.add(Dense(in_out_neurons,activation="linear"))
optimizer = Adam(lr=0.001)
model.compile(loss="mean_squared_error", optimizer=optimizer)
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm (LSTM)                 (None, 3, 128)            91648     
                                                                 
 dense (Dense)               (None, 3, 50)             6450      
                                                                 
=================================================================
Total params: 98,098
Trainable params: 98,098
Non-trainable params: 0
_________________________________________________________________

然后尝试训练和预测

print(final_x.shape) #(165737, 3, 50)
print(final_y.shape) #(165737, 1, 50)
model.fit(         
    final_x,final_y,
    batch_size=300,
    epochs=10,
    validation_split=0.9
)
print(test_input.shape) # (1, 3, 50)
predicted = model.predict(test_input)

显示错误ValueError: shapes (335476,50) and (3,50) not aligned: 50 (dim 1) != 3 (dim 0)

我不确定 335476 来自哪里......

我应该在哪里修复？？

Answer 1

您通常使用与训练原始模型相同的batch_size。有关此主题的更多信息和可能的解决方法，请访问 here。但是，由于您使用的是None，它应该适用于单个样本。这是一个工作示例：

import tensorflow as tf

length_of_sequence = 3
in_out_neurons = 50
n_hidden = 128
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(n_hidden, batch_input_shape=(None, length_of_sequence,in_out_neurons), return_sequences=True))
model.add(tf.keras.layers.Dense(in_out_neurons,activation="linear"))
optimizer = tf.keras.optimizers.Adam(lr=0.001)
model.compile(loss="mean_squared_error", optimizer=optimizer)
model.summary()

final_x = tf.random.normal((100, 3, 50))
final_y = tf.random.normal((100, 3, 50))
model.fit(         
    final_x,final_y,
    batch_size=2,
    epochs=10,
    validation_split=0.9
)

test_input = tf.random.normal((1, 3, 50))
predicted = model.predict(test_input)
print(predicted.shape)

(1, 3, 50)