shapes (335476,50) and (3,50) not aligned: 50 (dim 1) != 3 (dim 0)-CodePudding

my RNN is like this below

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
_________________________________________________________________

then try to train and predict

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)

shows the error ValueError: shapes (335476,50) and (3,50) not aligned: 50 (dim 1) != 3 (dim 0)

I am not sure wheere 335476 comes from....

Where should I fix ??

CodePudding user response：

You usually to use the same batch_size that you use to train your original model. More information on this topic and possible workarounds can be found here. However, since you are using None, it should work with a single sample. Here is a working example:

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)