Create LSTM.py

Keras/LSTM.py

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+import datetime
+import pandas as pd
+import matplotlib.pyplot as plt
+from pandas_datareader import data
+#pip install pandas-datareader
+
+stock = 'RENT3.SA'
+source = 'yahoo'
+
+# Set date range (Google went public August 19, 2004)
+start = datetime.datetime(2005, 8, 19)
+end = datetime.datetime(2019, 8, 6)
+
+# Collect Google stock data
+goog_df = data.DataReader(stock, source, start, end)
+
+dataset = goog_df['Adj Close']
+
+import tensorflow as tf
+import pandas as pd  
+import numpy as np
+from  sklearn.preprocessing import MinMaxScaler
+
+dataset = np.array(dataset.astype('float32')).reshape(-1,1)
+
+def norm(x):
+    return (x-np.min(x))/(np.max(x)-np.min(x))
+
+#dataset=norm(dataset)
+
+look_back=4
+np.random.seed(7)
+scaler = MinMaxScaler(feature_range=(0, 1))
+dataset = scaler.fit_transform(dataset)
+train_size = int(len(dataset) * 0.99)
+test_size = len(dataset) - train_size
+train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
+print(len(train), len(test))
+
+def create_dataset(dataset, look_back=look_back):
+	dataX, dataY = [], []
+	for i in range(len(dataset)-look_back):
+		a = dataset[i:(i+look_back), 0]
+		dataX.append(a)
+		dataY.append(dataset[i + look_back, 0])
+	return np.array(dataX), np.array(dataY)
+
+trainX, trainY = create_dataset(train, look_back)
+testX, testY = create_dataset(test, look_back)
+trainX
+
+trainY = trainY.reshape(len(trainY), 1)
+testY = testY.reshape(len(testY), 1)
+trainY
+
+X0=trainX
+Y0=trainY
+
+X0=X0.reshape(X0.shape[0],X0.shape[1],1)
+testX=testX.reshape(testX.shape[0],testX.shape[1],1)
+
+
+from tensorflow.compat.v1 import ConfigProto
+from tensorflow.compat.v1 import InteractiveSession
+
+config = ConfigProto()
+config.gpu_options.allow_growth = True
+session = InteractiveSession(config=config)
+
+class MyModel(tf.keras.Model):
+  def __init__(self, rnn_units):
+    super().__init__(self)
+    self.gru = tf.keras.layers.LSTM(rnn_units,activation='tanh', recurrent_activation='sigmoid',
+    use_bias=True, kernel_initializer='glorot_uniform',
+    recurrent_initializer='orthogonal',
+    bias_initializer='zeros', unit_forget_bias=True,
+    kernel_regularizer=None,
+                                   return_sequences=True, 
+                                   return_state=True)
+    self.dense = tf.keras.layers.Dense(1)
+
+  def call(self, inputs, states=None, return_state=False, training=False):
+    x=inputs
+    x, states, z = self.gru(x, initial_state=states, training=training)
+    x = tf.nn.relu(x)
+    pred = self.dense(x, training=training)
+
+#    if return_state:
+#      return pred, states
+#    else: 
+    return pred
+
+
+rnn_units = 40
+
+model = MyModel(
+    # Be sure the vocabulary size matches the `StringLookup` layers.
+    rnn_units=rnn_units)
+
+
+from tensorflow import keras
+loss=tf.keras.losses.MeanSquaredError()
+
+opt = keras.optimizers.Adam(learning_rate=0.01)
+model.compile(optimizer=opt, loss=loss,metrics='mae')
+
+checkpoint_dir = './training_checkpoints'
+import os 
+checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
+
+checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
+    filepath=checkpoint_prefix,
+    save_weights_only=True)
+
+EPOCHS=100
+
+history = model.fit(X0,Y0, epochs=EPOCHS, callbacks=[checkpoint_callback])
+
+import matplotlib.pyplot as plt 
+plt.plot(np.mean(model.predict(testX),axis=1).reshape(1,-1)[0])
+plt.plot(testY.reshape(1,-1)[0])
+plt.show()
+
+np.mean(abs(np.mean(model.predict(testX),axis=1).reshape(1,-1)[0]/testY.reshape(1,-1)[0]))