As the Error
suggests, the First Dimension
of X
and y
is different. First Dimension
indicates the Batch Size
and it should be same.
Please ensure that Y
also has the shape
, (1, something)
.
I could reproduce your error with the Code shown below:
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import tensorflow as tf
import numpy as np
# define sequences
sequences = [
[1, 2, 3, 4],
[1, 2, 3],
[1]
]
# pad sequence
padded = pad_sequences(sequences)
X = np.expand_dims(padded, axis = 0)
print(X.shape) # (1, 3, 4)
y = np.array([1,0,1])
#y = y.reshape(1,-1)
print(y.shape) # (3,)
model = Sequential()
model.add(LSTM(4, return_sequences=False, input_shape=(None, X.shape[2])))
model.add(Dense(1, activation='sigmoid'))
model.compile (
loss='mean_squared_error',
optimizer=tf.keras.optimizers.Adam(0.001))
model.fit(x = X, y = y)
If we observe the Print
Statements,
Shape of X is (1, 3, 4)
Shape of y is (3,)
This Error can be fixed by uncommenting the Line, y = y.reshape(1,-1)
, which makes the First Dimension
(Batch_Size
) equal (1
) for both X
and y
.
Now, the working code is shown below, along with the Output:
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import tensorflow as tf
import numpy as np
# define sequences
sequences = [
[1, 2, 3, 4],
[1, 2, 3],
[1]
]
# pad sequence
padded = pad_sequences(sequences)
X = np.expand_dims(padded, axis = 0)
print('Shape of X is ', X.shape) # (1, 3, 4)
y = np.array([1,0,1])
y = y.reshape(1,-1)
print('Shape of y is', y.shape) # (1, 3)
model = Sequential()
model.add(LSTM(4, return_sequences=False, input_shape=(None, X.shape[2])))
model.add(Dense(1, activation='sigmoid'))
model.compile (
loss='mean_squared_error',
optimizer=tf.keras.optimizers.Adam(0.001))
model.fit(x = X, y = y)
The Output of above code is :
Shape of X is (1, 3, 4)
Shape of y is (1, 3)
1/1 [==============================] - 0s 1ms/step - loss: 0.2588
<tensorflow.python.keras.callbacks.History at 0x7f5b0d78f4a8>
Hope this helps. Happy Learning!