Create 19_RNN.py

pages/19_RNN.py

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+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras import layers, models
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+
+# Load the dataset
+data_url = 'https://raw.githubusercontent.com/selva86/datasets/master/aapl.csv'
+df = pd.read_csv(data_url)
+df = df[['Date', 'Close']]
+df['Date'] = pd.to_datetime(df['Date'])
+df.set_index('Date', inplace=True)
+
+# Normalize the data
+scaler = MinMaxScaler(feature_range=(0, 1))
+scaled_data = scaler.fit_transform(df)
+
+# Create sequences
+def create_sequences(data, seq_length):
+    xs = []
+    ys = []
+    for i in range(len(data) - seq_length):
+        x = data[i:i + seq_length]
+        y = data[i + seq_length]
+        xs.append(x)
+        ys.append(y)
+    return np.array(xs), np.array(ys)
+
+seq_length = 60
+X, y = create_sequences(scaled_data, seq_length)
+
+# Split the data into training and testing sets
+split = int(0.8 * len(X))
+X_train, X_test = X[:split], X[split:]
+y_train, y_test = y[:split], y[split:]
+
+# Reshape data for the model
+X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
+X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
+
+# Build the RNN model
+model = models.Sequential()
+model.add(layers.LSTM(50, return_sequences=True, input_shape=(seq_length, 1)))
+model.add(layers.LSTM(50, return_sequences=False))
+model.add(layers.Dense(25))
+model.add(layers.Dense(1))
+
+model.summary()
+
+# Compile the model
+model.compile(optimizer='adam', loss='mean_squared_error')
+
+# Train the model
+history = model.fit(X_train, y_train, batch_size=32, epochs=20, validation_split=0.1)
+
+# Make predictions
+train_predict = model.predict(X_train)
+test_predict = model.predict(X_test)
+
+# Inverse transform the predictions
+train_predict = scaler.inverse_transform(train_predict)
+y_train = scaler.inverse_transform(y_train.reshape(-1, 1))
+test_predict = scaler.inverse_transform(test_predict)
+y_test = scaler.inverse_transform(y_test.reshape(-1, 1))
+
+# Plot the results
+plt.figure(figsize=(14, 5))
+plt.plot(df.index, df['Close'], label='True Price')
+plt.plot(df.index[seq_length:seq_length + len(train_predict)], train_predict, label='Train Predict')
+plt.plot(df.index[seq_length + len(train_predict):], test_predict, label='Test Predict')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.legend()
+plt.show()