ajout d'une première approche par LSTM, non fonctionnelle. Pas de validation

code/RNN/Model.py

@@ -0,0 +1,20 @@
+import tensorflow as tf
+
+class MyModel(tf.keras.Model):
+
+	def __init__(self, HIDDEN):
+		super(MyModel, self).__init__()
+		self.lstm1 = tf.keras.layers.LSTM(HIDDEN, return_sequences=True)
+		self.lstm2 = tf.keras.layers.LSTM(HIDDEN, return_sequences=True)
+		#self.lstm3 = tf.keras.layers.LSTM(HIDDEN, return_sequences=True)
+		#self.lstm4 = tf.keras.layers.LSTM(HIDDEN, return_sequences=True)
+		self.lstmlast = tf.keras.layers.LSTM(1, return_sequences=True)
+
+
+	def call(self, inputs):
+		x = self.lstm1(inputs)
+		x = self.lstm2(x)
+		#x = self.lstm3(x)
+		#x = self.lstm4(x)
+		x = self.lstmlast(x)
+		return x

code/RNN/grab_data.py

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+import pandas
+import numpy as np
+
+csv = pandas.read_csv("WILL5000INDFC.csv")
+extraction = csv["WILL5000INDFC"].tolist()
+for i in range(len(extraction)) :
+	if extraction[i] == "." :
+		extraction[i] = extraction[i-1]
+extraction = np.array(extraction).astype(float)
+print(extraction)
+np.save("dataset.npy", extraction)

code/RNN/training.py

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+import tensorflow as tf
+from matplotlib import pyplot as plt
+import numpy as np
+from Model import MyModel
+
+LEN_TRAIN = 5000
+LEN_SEQ = 100
+PRED = 0.02
+HIDDEN = 128
+
+model = MyModel(HIDDEN)
+
+dataset = np.load('dataset.npy')
+datasetp = np.roll(dataset, 1)
+datasetp[0] = dataset[0]
+data = (dataset - datasetp)/datasetp
+data = data*2/(max(data) - min(data))
+
+annee = np.array(list(range(len(data))))/365
+annee = annee - annee[-1]
+start_pred = int(len(data)*(1-PRED))
+print(len(data))
+print(start_pred)
+plt.figure(1)
+plt.plot(annee[:start_pred],data[:start_pred], label="apprentissage")
+plt.plot(annee[start_pred:],data[start_pred:], label="validation")
+plt.legend()
+plt.show()
+
+X_train = [data[0:start_pred-1]]
+Y_train = [data[1:start_pred]]
+
+X_train = np.expand_dims(np.array(X_train),2)
+Y_train = np.expand_dims(np.array(Y_train),2)
+
+
+model.compile(optimizer='adam',
+			  loss='binary_crossentropy',
+			  metrics=['binary_crossentropy'])
+import os 
+os.system("rm -rf log_dir")
+
+model.fit(x=X_train, y=Y_train, epochs=30)
+
+Pred = X_train.copy()
+while len(Pred[0]) < len(data) :
+	print(len(data) - len(Pred[0]))
+	Pred = np.concatenate((Pred, np.array([[model.predict(Pred)[0][-1]]])),1)
+Pred = Pred/2*(max(data) - min(data))
+data_Pred = dataset.copy()
+Pred = np.squeeze(Pred)
+for i in range(start_pred,len(data_Pred)) :
+	data_Pred[i] = data_Pred[i-1]*Pred[i] + data_Pred[i-1]
+
+plt.figure(2)
+plt.plot(annee[:start_pred],dataset[:start_pred], label="apprentissage")
+plt.plot(annee[start_pred:],dataset[start_pred:], label="validation")
+plt.plot(annee, data_Pred, label="prediction")
+plt.legend()
+
+fig, axs = plt.subplots(2)
+axs[0].plot(annee[:start_pred],dataset[:start_pred], label="apprentissage")
+axs[0].plot(annee[start_pred:],dataset[start_pred:], label="validation")
+axs[1].plot(annee, data_Pred, label="prediction")
+plt.legend()
+plt.show()