[ad_1] import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, LSTM from sklearn.preprocessing import MinMaxScaler import numpy as np # Load and preprocess data # … # Build the neural network model = Sequential() model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(50, return_sequences=False)) model.add(Dense(25)) model.add(Dense(1)) # Compile the model model.compile(optimizer=”adam”, loss=”mean_squared_error”) # Train the
[ad_1] registerEA( “nn_example”, “A test EA to run neuron model for XOR(v1.0)”, [ {name: “h11”, value: 20, required: true, type: “Number”, range: [-100, 100], step: 10}, {name: “h12”, value: 20, required: true, type: “Number”, range: [-100, 100], step: 10}, {name: “b1”, value: -10, required: true, type: “Number”, range: [-100, 100], step: 10},
[ad_1] An artificial neural network is made up of several layers of interconnected “neurons”, each layer having a specific role in the data processing process. The three main types of layers are: Input Layer: This layer receives the raw data as input. In the context of trading, this can be historical data such as open