How to shift output of predict values into the x1 (input) column 0 values using Neural network in python
$begingroup$
The inputs here are the 3. The output here (LSTM) is the probabilities that the next x1 input ought to be.
Means here I have x,x1,x2 input values. 1st three inputs LSTM output1 and then next if x value = 0 then Lstm output1 is back into as input and predict next output2. If this output 2 value is equal to next x value then back into as input and predict output 3. If not equal not to take output 2 as x input and take it as mentioned x input. The output (Yt) at timestep t depends on the input X(t) and on the previous output Y(t-1).
as a example
x x1 x2 predict (output)
60 0 30 260
0 20 0 130
110 10 0 160
0 0 10 100
here second value of x1 column is 0, x value = 0 then take the value as output 1
x = output1
here x column 3rd value measured and output 2 value is not equal to x 3rd value.
then take input as measured value.
So this is the method that I want to do. But I don't know how to write it. Can anyone helps me to do it?
my LSTM code:
fit1 = Sequential ()
fit1.add(LSTM(32, return_sequences=True, activation='relu',input_shape=(3,1)))
fit1.add((LSTM(32, return_sequences=True)))
fit1.add(LSTM(32))
fit1.add(Dense(1))
batchsize = 3
fit1.compile(loss="mean_squared_error",optimizer="adam")
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10,shuffle=True)
pred1=fit1.predict(x_test)
for i in range(len(x)):
pred1.append(fit1.predict([x[i,None,:],pred1[i]]))
pred1 = np.asarray(pred1)
pred1 = pred1.reshape(pred1.shape[0],pred1.shape[2])
But this is not working. not having relationship inbetween input and output.
error:
python neural-network keras tensorflow lstm
asked 1 min ago
kaskas
617
$endgroup$
add a comment |
$begingroup$
The inputs here are the 3. The output here (LSTM) is the probabilities that the next x1 input ought to be.
Means here I have x,x1,x2 input values. 1st three inputs LSTM output1 and then next if x value = 0 then Lstm output1 is back into as input and predict next output2. If this output 2 value is equal to next x value then back into as input and predict output 3. If not equal not to take output 2 as x input and take it as mentioned x input. The output (Yt) at timestep t depends on the input X(t) and on the previous output Y(t-1).
as a example
x x1 x2 predict (output)
60 0 30 260
0 20 0 130
110 10 0 160
0 0 10 100
here second value of x1 column is 0, x value = 0 then take the value as output 1
x = output1
here x column 3rd value measured and output 2 value is not equal to x 3rd value.
then take input as measured value.
So this is the method that I want to do. But I don't know how to write it. Can anyone helps me to do it?
my LSTM code:
fit1 = Sequential ()
fit1.add(LSTM(32, return_sequences=True, activation='relu',input_shape=(3,1)))
fit1.add((LSTM(32, return_sequences=True)))
fit1.add(LSTM(32))
fit1.add(Dense(1))
batchsize = 3
fit1.compile(loss="mean_squared_error",optimizer="adam")
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10,shuffle=True)
pred1=fit1.predict(x_test)
for i in range(len(x)):
pred1.append(fit1.predict([x[i,None,:],pred1[i]]))
pred1 = np.asarray(pred1)
pred1 = pred1.reshape(pred1.shape[0],pred1.shape[2])
But this is not working. not having relationship inbetween input and output.
error:
python neural-network keras tensorflow lstm
asked 1 min ago
kaskas
617
$endgroup$
add a comment |
$begingroup$
The inputs here are the 3. The output here (LSTM) is the probabilities that the next x1 input ought to be.
Means here I have x,x1,x2 input values. 1st three inputs LSTM output1 and then next if x value = 0 then Lstm output1 is back into as input and predict next output2. If this output 2 value is equal to next x value then back into as input and predict output 3. If not equal not to take output 2 as x input and take it as mentioned x input. The output (Yt) at timestep t depends on the input X(t) and on the previous output Y(t-1).
as a example
x x1 x2 predict (output)
60 0 30 260
0 20 0 130
110 10 0 160
0 0 10 100
here second value of x1 column is 0, x value = 0 then take the value as output 1
x = output1
here x column 3rd value measured and output 2 value is not equal to x 3rd value.
then take input as measured value.
So this is the method that I want to do. But I don't know how to write it. Can anyone helps me to do it?
my LSTM code:
fit1 = Sequential ()
fit1.add(LSTM(32, return_sequences=True, activation='relu',input_shape=(3,1)))
fit1.add((LSTM(32, return_sequences=True)))
fit1.add(LSTM(32))
fit1.add(Dense(1))
batchsize = 3
fit1.compile(loss="mean_squared_error",optimizer="adam")
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10,shuffle=True)
pred1=fit1.predict(x_test)
for i in range(len(x)):
pred1.append(fit1.predict([x[i,None,:],pred1[i]]))
pred1 = np.asarray(pred1)
pred1 = pred1.reshape(pred1.shape[0],pred1.shape[2])
But this is not working. not having relationship inbetween input and output.
error:
python neural-network keras tensorflow lstm
asked 1 min ago
kaskas
617
$endgroup$
The inputs here are the 3. The output here (LSTM) is the probabilities that the next x1 input ought to be.
Means here I have x,x1,x2 input values. 1st three inputs LSTM output1 and then next if x value = 0 then Lstm output1 is back into as input and predict next output2. If this output 2 value is equal to next x value then back into as input and predict output 3. If not equal not to take output 2 as x input and take it as mentioned x input. The output (Yt) at timestep t depends on the input X(t) and on the previous output Y(t-1).
as a example
x x1 x2 predict (output)
60 0 30 260
0 20 0 130
110 10 0 160
0 0 10 100
here second value of x1 column is 0, x value = 0 then take the value as output 1
x = output1
here x column 3rd value measured and output 2 value is not equal to x 3rd value.
then take input as measured value.
So this is the method that I want to do. But I don't know how to write it. Can anyone helps me to do it?
my LSTM code:
fit1 = Sequential ()
fit1.add(LSTM(32, return_sequences=True, activation='relu',input_shape=(3,1)))
fit1.add((LSTM(32, return_sequences=True)))
fit1.add(LSTM(32))
fit1.add(Dense(1))
batchsize = 3
fit1.compile(loss="mean_squared_error",optimizer="adam")
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10,shuffle=True)
pred1=fit1.predict(x_test)
for i in range(len(x)):
pred1.append(fit1.predict([x[i,None,:],pred1[i]]))
pred1 = np.asarray(pred1)
pred1 = pred1.reshape(pred1.shape[0],pred1.shape[2])
But this is not working. not having relationship inbetween input and output.
error:
python neural-network keras tensorflow lstm
python neural-network keras tensorflow lstm
asked 1 min ago
kaskas
617
asked 1 min ago
kaskas
617
asked 1 min ago
kaskas
617
asked 1 min ago
kaskas
617
asked 1 min ago
kaskas
617
617
add a comment |
add a comment |
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