CODEWITHSUNDEEP
pythontensorflowkerasdeep-learninglstm
Mahdi
Mahdi

Reputation: 341

Using dynamic input shape in keras

I am working with some data that contains some features in some continues days and the shape of the array of each of these data is as below:
(number of days, 1, number of features)

Number of features in each of these data is different.
I want to feed each of these data, separately to my lstm model. So I want to implement my model in a way that its input shape is dynamic.

I have used this code:

model = Sequential()
model.add(LSTM(4, return_sequences=True, input_shape=(1, None)))
model.add(LSTM(256, dropout=0.2,recurrent_dropout=0.2, return_sequences=True))
model.add(LSTM(256, dropout=0.2,recurrent_dropout=0.2, return_sequences=True))
model.add(LSTM(128, dropout=0.2,recurrent_dropout=0.2, return_sequences=True))
model.add(LSTM(128))
model.add(Dense(1, activation='sigmoid'))

model.compile (
    loss='mean_squared_error',
    optimizer=keras.optimizers.Adam(0.001)
)

That None in the first layer is for number of features. But I get this error for this layer when I start to fit the model on (X_train and y_train):

TypeError: unsupported operand type(s) for +: 'NoneType' and 'int'

I am using tensorflow version '2.3.0-tf'

Can you help me to fix this error?

Upvotes: 1

Views: 3668

Answers (1)

user11530462
user11530462

Reputation:

The Function, pad_sequences will be useful in this case.

For example, Input Sequences has different number of Features as shown below:

sequences = [
    [1, 2, 3, 4],
       [1, 2, 3],
             [1]
    ]

We can make all the Features of equal length using pad_sequences, as shown below:

padded = pad_sequences(sequences)

That will make Input Sequence:

[[1 2 3 4]
 [0 1 2 3]
 [0 0 0 1]]

That is, it will pad the other Features with Zeros and will make the Number of Features of all the Samples as 4 (Maximum among them).

The Padding with Zeros can be done at the start or at the end by adjusting the argument, 'padding'. For more details about this Function, please refer this Tensorflow Documentation.

Complete working code with variable number of Features is 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)
print(padded)
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) # (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)

Output of above code is :

[[1 2 3 4]
 [0 1 2 3]
 [0 0 0 1]]

(1, 3, 4)

(1, 3)

1/1 [==============================] - 0s 1ms/step - loss: 0.2601

Hope this helps. Happy Learning!

Upvotes: 2

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