MLA_Learn_Periodic_Functions/code/RNN/training.py

import tensorflow as tf
from matplotlib import pyplot as plt
import numpy as np
from Model import MyModel


LEN_SEQ = 64
PRED = 0.05
HIDDEN = 128

model = MyModel(HIDDEN)

dataset = np.load('dataset.npy')
scale = (np.max(dataset) - np.min(dataset))
data = dataset/scale
shift = np.min(data)
data = data - shift

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_tot = [data[0:start_pred-1]]
Y_train_tot = [data[1:start_pred]]

X_train_tot = np.expand_dims(np.array(X_train_tot),2)
Y_train_tot = np.expand_dims(np.array(Y_train_tot),2)

X_train = X_train_tot[:,:LEN_SEQ,:]
Y_train = Y_train_tot[:,:LEN_SEQ,:]

for i in range(len(X_train_tot[0]) - LEN_SEQ) :
	X_train = np.concatenate((X_train, X_train_tot[:,i:i+LEN_SEQ,:]),0)
	Y_train = np.concatenate((Y_train, Y_train_tot[:,i:i+LEN_SEQ,:]),0)
print(X_train_tot.shape)
print(X_train.shape)


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, batch_size=16, epochs=5, shuffle=True)

Pred = X_train_tot.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 + shift
Pred = Pred * scale

data_Pred = np.squeeze(Pred)

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()