Input error for LSTM neural network

[BRN]

Hi everyone,

I have to classify a DNA sequence with a LSTM neural network but I have a problem with the inputs shame. Both the sequence and the class are encoded with One Hot Encoding and my code is this:

import pandas as pd
import numpy as np

data = pd.read_csv('splice.data', header = None)

data_shuffled = data.sample(frac = 1).reset_index(drop = True)

# space removing
for i in range (len(data)):
    data.loc[i][2] = data.loc[i][2].strip()

raw_data = np.array(data)

def one_hot_encoder(data):
    x = []
    y = []
    
    for i in range (len(data)):
        oh_class = np.zeros((1, 3))
        if data[i][0] == 'EI': oh_class[0][0] = 1
        elif data[i][0] == 'IE': oh_class[0][1] = 1
        else: oh_class[0][2] = 1
            
        y.append(oh_class)
            
        oh_seq = np.zeros((len(data[0][2]), 4))
        for j in range (len(data[0][2])):
            if data[i][2][j] == 'A': oh_seq[j][0] = 1
            elif data[i][2][j] == 'C': oh_seq[j][1] = 1
            elif data[i][2][j] == 'G': oh_seq[j][2] = 1
            else: oh_seq[j][3] = 1
            
        x.append(oh_seq)
                            
    return np.array(x), np.array(y)     

x_seq, y_class = one_hot_encoder(raw_data)

# Split into validation and training data
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x_seq, y_class, test_size = 0.2, random_state = 1)

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout

#Initialize the RNN

model = Sequential()
model.add(LSTM(units = 50, activation = 'relu', return_sequences = True, input_shape = (x_train.shape[1], x_train.shape[2])))
model.add(Dropout(0.2)) 
model.add(LSTM(units = 60, activation = 'relu', return_sequences = True))
model.add(Dropout(0.3)) 
model.add(LSTM(units = 80, activation = 'relu', return_sequences = True))
model.add(Dropout(0.4)) 
model.add(LSTM(units = 120, activation = 'relu'))
model.add(Dropout(0.5)) 
model.add(Dense(units = 3, activation='softmax'))
model.summary()

model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.fit(x_train, y_train, epochs = 100, batch_size = 80, validation_split = 0.1)

The error I receive is this

ValueError: Shapes (None, 1, 3) and (None, 3) are incompatible

Can anyone tell me how to solve?

Thanks!

 

[phyzguy]

Somewhere you are trying to equate or merge (or somehow deal with) two arrays of different shapes. Which line is giving the error you pasted? Please include the whole error message.

 

[BRN]

I'm sorry if I answer only now.

I resolved the issue and yes, and I had two arrays with different shapes. Now, I discover "reshape" function in numpy.

I'm a newbie with Python ...

Anyway, thanks for your concern!