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LSTM using tensorflow

Poor Porcupine answered on July 27, 2023 Popularity 9/10 Helpfulness 6/10

answer LSTM using tensorflow

LSTM using tensorflow

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import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

# Assuming you have the following data
# X_train: Training features for all wells (shape: [num_wells, time_steps, num_features])
# y_train: Training labels (months to first oil production) for all wells (shape: [num_wells,])
# X_test: Testing features for all wells (shape: [num_wells, time_steps, num_features])

# Normalize the features
scaler = MinMaxScaler(feature_range=(0, 1))
X_train = scaler.fit_transform(X_train.reshape(-1, X_train.shape[-1])).reshape(X_train.shape)
X_test = scaler.transform(X_test.reshape(-1, X_test.shape[-1])).reshape(X_test.shape)

# Define the LSTM model
model = Sequential()
model.add(LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dense(1, activation='linear'))

# Compile the model
model.compile(loss='mean_squared_error', optimizer='adam')

# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)

# Evaluate the model on the testing data
loss = model.evaluate(X_test, y_test)
print("Test Loss:", loss)

# Make predictions for new wells
predictions = model.predict(X_new_wells)

Popularity 9/10 Helpfulness 6/10 Language python

Source: chat.openai.com

Tags: lstm python tensorflow using

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Contributed on Jul 27 2023

Poor Porcupine

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LSTM using tensorflow

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