Trained three different models nonstop

graphs.py

+import matplotlib.pyplot as plt
+import numpy as np
+from util import read_list
+
+def plot(loss, label=None):
+	x = np.arange(0, len(loss))
+	plt.plot(loss, label=label)
+	plt.xlabel('Epochs')
+	plt.ylabel('Loss')
+
+def plot_best_model():
+	loss = [2.6129, 2.4543, 2.3803, 2.3188, 2.2835, 2.2500, 2.2371, 2.2178, 2.2071, 2.1918, 2.1795, 2.1839, 2.1855, 2.1756, 2.1869, 2.0661, 2.08390, 2.0756, 2.0439, 2.0376, 2.0501, 2.0166, 1.9766, 1.9441, 1.9409, 1.9263 , 1.9020, 1.8844, 1.8782, 1.8675, 1.8649, 1.8323, 1.8085, 1.7909, 1.7810, 1.7713, 1.7688, 1.7230, 1.7030, 1.6947, 1.6947, 1.6292, 1.5968, 1.5830]
+	plot(loss, 'Best model')
+
+def plot_lstm_50_model():
+	loss = [2.6357, 2.5246, 2.4718, 2.4106, 2.3716, 2.3487, 2.3232, 2.3089, 2.2949, 2.2901, 2.2137, 2.2068, 2.2021, 2.1978, 2.1933, 2.2126, 2.2105, 2.2200]
+	plot(loss)
+
+def plot_rnn_val_loss():
+	loss = [2.5573, 2.4642, 2.4089, 2.3709, 2.3442, 2.3265, 2.3136, 2.3000, 2.2965, 2.2883, 2.2848, 2.2210, 2.1682, 2.1604, 2.1587, 2.1231, 2.0898, 2.0942, 2.1014, 2.1023, 2.1073, 2.1093, 2.1107, 2.1167, 2.1199, 2.1216, 2.1240]
+	plot(loss, 'Validation')
+
+def plot_rnn_train_loss():
+	loss = [2.7632, 2.5124, 2.4267, 2.3725, 2.3330, 2.3032, 2.2820, 2.2667, 2.2471, 2.2353, 2.2209, 2.1950, 2.1639, 2.1431, 2.1302, 2.1260, 2.1147, 2.1086, 2.1036, 2.0993, 2.0946, 2.0899, 2.0858, 2.0806, 2.0766, 2.0717, 2.0675]
+	plot(loss, 'Training')
+
+
+def plot_lstm_nonstop():
+	hist100 = read_list('lstm_30_epochs_nonstop_dropout/history_69')
+	hist50 = read_list('lstm_70_epochs_50_units_nonstop_dropout/history_69')
+	hist_RNN = read_list('rnn_50_epochs_nonstop_dropout/history_69')
+
+	plot(hist100['val_loss'], 'Validation LSTM 100')
+	plot(hist100['loss'], 'Training LSTM 100')
+	plot(hist50['val_loss'], 'Validation LSTM 50')
+	plot(hist50['loss'], 'Training LSTM 50')
+	plot(hist_RNN['val_loss'], 'Validation RNN 100')
+	plot(hist_RNN['loss'], 'Training RNN 100')
+
+# plot_best_model()
+# # plot_lstm_50_model()
+# plot_rnn_val_loss()
+# plot_rnn_train_loss()
+
+plot_lstm_nonstop()
+plt.legend()
+plt.show()
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