I want to use MLPs to solve regression problem.
I have inputs with variable length to fix this I want to use Zero-padding with masking layer.
I read the inputs from csv file using pandas library. Here is how my data look like.
I know only how to fill NaN values with 0 using this command x_train.fillna(0.0).values
Like the first row :
[4, 0, 0, 512, 1.0, 0.0, 1.0, 0.0, 128.0 , NaN]
After padding :
[4, 0, 0, 512, 1.0, 0.0, 1.0, 0.0, 128.0 , 0.0]
The mask should be like this :
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
But I do not know how to add the mask layer and feed them into my MLPs.
If i have fixed input length. My program will look like this
...
n_input = 10 #number og inputs
train_X = pd.read_csv('x_train.csv')
train_Y = pd.read_csv('y_train.csv')
X = tf.placeholder("float", [None, n_input])
Y = tf.placeholder("float", [None, n_output])
...
y_pred = multilayer_perceptron(X)
...
with tf.Session() as sess:
sess.run(init)
_, c = sess.run([train, loss], feed_dict={X: train_X,
Y: train_Y})
...
I do not know how to combine between Zero padding and masking layer?
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