Created
November 8, 2018 13:31
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myrnn_retur_sequences_true.py
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| model_myrnn_rst = RecurrentNeuralNetwork(rnn_units, return_sequences=True) | |
| model_myrnn_rst.fit(X_train, Y_train, | |
| batch_size=batch_size, | |
| epochs=n_epochs, | |
| mu=lr, | |
| validation_data=(X_val, Y_val), | |
| verbose=1) | |
| plt.plot(indices, history_rst.history["loss"], label="loss (keras)") | |
| plt.plot(indices, history_rst.history["val_loss"], label="val_loss (keras)") | |
| plt.plot(indices, model_myrnn_rst.loss, label="loss (my rnn)") | |
| plt.plot(indices, model_myrnn_rst.val_loss, label="val_loss (my rnn)") | |
| plt.legend(loc="best") | |
| plt.title("train history") | |
| plt.xlabel("epochs") | |
| plt.ylabel("loss") | |
| plt.grid(True) | |
| plt.show() | |
| Y_pred_myrnn_rst = model_myrnn_rst.predict(X) | |
| plt.plot(T, Y[:, -1, :], label="true") | |
| plt.plot(T, Y_pred_rst[:, -1, :], label="pred (keras)") | |
| plt.plot(T, Y_pred_myrnn_rst[:, -1, :], label="pred (my rnn)") | |
| plt.legend(loc='best') | |
| plt.title("true and pred") | |
| plt.xlabel("time") | |
| plt.ylabel("amplitude") | |
| plt.xlim([0,1]) | |
| plt.ylim([-2,2]) | |
| plt.grid(True) | |
| plt.show() |
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