2bit x 12 . के अनुक्रमों को दोहराने के लिए प्रशिक्षण प्रगति नहीं करता है

मैं simpleRNN मॉडल का परीक्षण कर रहा हूँ

मेरा परीक्षण डेटा सुपर सरल दोहराए जाने वाला ट्रिपल 2 बिट * 12 . है

[1,0,0,0,1,0,0,1,0,0,0,0],[1,0,0,0,0,1,0,0,0,1,0,0],[0,0,1,0,0,0,0,1,0,0,0,1]

for i in range(0,100):
    temp = [[1,0,0,0,1,0,0,1,0,0,0,0],
        [1,0,0,0,0,1,0,0,0,1,0,0],
        [0,0,1,0,0,0,0,1,0,0,0,1]]
    line.extend(temp)
total = []
for j in range(0,500):
    total.append(line)
total = np.array(total)
print(total.shape) # (500, 300, 12)

यह (500, 300, 12) को सुन्न कर देता है। सभी डेटा बस दोहरा रहे हैं, इसलिए मुझे उम्मीद है कि प्रशिक्षण और भविष्यवाणी पूरी तरह से काम करेगी।

हालांकि val_loss घटता नहीं है और भविष्यवाणी प्रभावी रूप से काम नहीं करती है।

Epoch 1/5
743/743 [==============================] - 5s 5ms/step - loss: 0.1386 - val_loss: 0.1305
Epoch 2/5
743/743 [==============================] - 3s 4ms/step - loss: 0.1305 - val_loss: 0.1294
Epoch 3/5
743/743 [==============================] - 3s 4ms/step - loss: 0.1299 - val_loss: 0.1292
Epoch 4/5
743/743 [==============================] - 3s 4ms/step - loss: 0.1300 - val_loss: 0.1291
Epoch 5/5
743/743 [==============================] - 3s 4ms/step - loss: 0.1299 - val_loss: 0.1293
[[ 0.67032564 -0.0020391   0.3332582  -0.0095186   0.35370785  0.3042156
   0.00809216  0.7059332   0.00199411  0.30952734 -0.0021943   0.333712  ]]
tf.Tensor([[1 0 0 0 0 0 0 1 0 0 0 0]], shape=(1, 12), dtype=int32)

मुझे उम्मीद है कि परिणाम स्मिलर [1,0,0,0,1,0,0,1,0,0,0,0] के रूप में होगा, हालांकि,,,,

मुझे कहीं बदलने की जरूरत है या मेरा कोड गलत है ??

ये मेरी पूरी डोर हैं।

import tensorflow as tf
from django.core.management.base import BaseCommand, CommandError
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM,Dropout,Dense
from tensorflow.keras.layers import SimpleRNN
import numpy as np
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split

def makeModel(input_len,n_in):
    n_hidden = 512
    model = Sequential()
    model.add(SimpleRNN(n_hidden, input_shape=(input_len, n_in), return_sequences=False))
    model.add(Dense(n_hidden, activation="relu")) 
    model.add(Dense(n_in, activation="relu"))
    opt = Adam(lr=0.001)
    model.compile(loss='mse', optimizer=opt)
    model.summary()
    return model
 

line = []
    
for i in range(0,100):
    temp = [[1,0,0,0,1,0,0,1,0,0,0,0],
        [1,0,0,0,0,1,0,0,0,1,0,0],
        [0,0,1,0,0,0,0,1,0,0,0,1]]
    line.extend(temp)
total = []
for j in range(0,500):
    total.append(line)
total = np.array(total)
print(total.shape) # (500, 300, 12)

chordIdList = total
n_in = 12 # dimention
input_len = 3 # length to use prediction.
model = makeModel(input_len,n_in)

input_=[]
target_=[]
for C in chordIdList:
    for i in range(0, len(C) - input_len):  
        input_.append( C[i:i+input_len] )  
        target_.append( C[i+input_len] )  
        
X = np.array(input_)
Y = np.array(target_).reshape((len(input_),1,n_in))

from sklearn.model_selection import train_test_split
x, x_val,y, y_val = train_test_split(X, Y, train_size=0.8, random_state=1)
print(x.shape) # (23760, 3, 12)
print(y.shape) # (23760, 1, 12)
print(x_val.shape) #(5940, 3, 12)
print(y_val.shape) # (5940, 1, 12)

epoch = 5
history = model.fit(x, y, epochs=epoch,validation_data=(x_val, y_val))

in_ = np.array([[1,0,0,0,1,0,0,1,0,0,0,0],
        [1,0,0,0,0,1,0,0,0,1,0,0],
        [0,0,1,0,0,0,0,1,0,0,0,1]]).reshape(1,3,12) 
        
print(in_.shape)
out_ = model.predict(in_)
print(out_)