Prediction in non classified answer - tensorflow

Question

I have create neuronetwork in Kerars, program is runing but there is problem of result, it is Forexforcast network in forcast it should return 0 or 1 , as provided in traing dataset but result is showing in between 0 and 1 in float like "[[0.47342286]]"
I have tried to use numpy athmax but it only result in 1 answer
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime
from sklearn.preprocessing import MinMaxScaler
from ta import *
dataset = pd.read_csv('C:/Users/SIGMA COM/PycharmProjects/deep/GBP_JPY Historical Data.csv',index_col="Date",parse_dates=True)
dataset = dataset[::-1]
print(dataset.head())
print(dataset.isna().any())
print(dataset.info())
dataset['Open'].plot(figsize=(16,6))
# initial value
step_size = 4
batch_sizes = 1
dataset['Diff'] = dataset['Open'] - dataset['Price']
dataset['Range'] = dataset['High'] - dataset['Low']
dataset['Rsi'] = rsi(close=dataset['Price'],n=4,fillna=True)
dataset['Macd'] = macd(close=dataset['Price'],n_fast=12,n_slow=26,fillna=True)
dataset['Cci'] = cci(high=dataset['High'],low=dataset['Low'],close=dataset['Price'],n=20,fillna=True)
# dataset['Rsi'] = dataset['Rsi'] /100.0
# # dataset['Macd'] = dataset['Macd'] /2.0
# dataset['Cci'] = dataset['Cci'] / 500.0
training_set = dataset[['Rsi','Macd','Cci','Price','Low','High','Open','Signal']]
sc = MinMaxScaler()
training_set_scaled = sc.fit_transform(training_set)
# Creating a data structure with 60 timesteps and 1 output
X_train = []
y_train = []
for i in range(60, 1258):
X_train.append(training_set_scaled[i-60:i, 0])
y_train.append(training_set_scaled[i, -1:])
X_train, y_train = np.array(X_train), np.array(y_train)
# Reshaping
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
print(X_train.shape)
print(X_train)
plt.show()
# Part 2 - Building the RNN
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
print((X_train.shape[1], 1))
print(X_train.shape)
# Initialising the RNN
regressor = Sequential()
# Adding the first LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
regressor.add(Dropout(0.2))
# Adding a second LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
# Adding a third LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
# Adding a fourth LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))
# Adding the output layer
regressor.add(Dense(units = 1,activation='sigmoid'))
# Compiling the RNN
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
# Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 10, batch_size = 32)
result = regressor.predict(np.reshape(X_train[100],(1,60,1)))
print(result)
I want to make model to make predication in class 0 and 1

Answer 1

This behavior is expected, because the sigmoid function is going to return a number between zero and one, like so:
So if your class labels are either 0 or 1, which seems to be the case here, for a binary classification problem you can just round the resultant output for your class prediction. Let's make a distinction between a classification vs. a regression problem here: regression is like finding the "line of best fit;" that is, the model is being trained to approximate the data. This appears to be what you're doing here: you're minimizing the mean squared error and searching for the model that best approximates your data, but that doesn't make a prediction.
If you want to actually make a classification, you can just round all elements of the result of regressor.predict to 0 or 1, and then compare your predictions with the true labels. This can actually be done easily in numpy like so: numpy.around(your_predictions, decimals=0). Note the decimals argument is not strictly required since it defaults to a value of 0, it's nice for clarity.
As for using numpy.argmax (I'm going to assume that's what you meant by athmax since I can't find a function with that spelling), it will give you the same label for everything because it returns the index of the largest element in an array. Since your output array has length one (because it's simply a single neuron that calculates the logistic function), it will always return index zero! However, you're sort of on the right track: if your last layer was instead Dense(units=n_classes, activation='softmax') — softmax outputs a probability distribution that a particular row of data will produce each label. In that case, numpy.argmax is correct.
Here's a Tensorflow tutorial on classification that I found super helpful when I was just learning it myself. It uses softmax instead of sigmoid like you, but I think it's fairly adaptable to your needs: https://www.tensorflow.org/tutorials/keras/basic_classification
Hope this helps!