CircularRR_AOPS_Forward_model

This code is corresponding to the Forward Deep Neural Network (DNN) section of the article.
Please cite the paper in any publication using this code.

CircularRR_AOPS_Forward_model.py

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+"""
+The code for the research presented in the paper titled "A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches
+
+@authors: Ehsan Adibnia, Majid Ghadrdan and Mohammad Ali Mansouri-Birjandi
+Corresponding author: [email protected]
+
+This code is corresponding to the Forward Deep Neural Network (DNN) section of the article.
+Please cite the paper in any publication using this code.
+"""
+import numpy as np 
+import matplotlib.pyplot as plt 
+import pandas as pd 
+import pickle
+import csv
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from keras.models import Sequential 
+from keras.layers import Dense
+from keras.layers import LeakyReLU
+from keras.layers import Dropout
+from keras.callbacks import EarlyStopping
+from keras.optimizers import Adadelta
+
+### Load the data from CSV file (the results of FDTD solver)
+result = pd.read_csv("result_V.csv", header=None)
+result = result.to_numpy()
+
+x = result[0:result.shape[0],0:6]
+y = result[0:result.shape[0],6:8]
+
+# Allocation of 70% of the total data to the training data
+x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.30, shuffle='true')
+# Allocation of 50% of the remaining data to the validateion data and 50% to the test data (15% validation and 15% test of total)
+x_test, x_val, y_test, y_val = train_test_split(x_val, y_val, test_size=0.50, shuffle='true')
+
+### Feature Scaling
+sc = StandardScaler()
+x_train = sc.fit_transform(x_train)
+x_val = sc.transform(x_val)
+x_test = sc.transform(x_test)
+
+# To prevent the RAM from being filled up, we delete the result.
+del result
+### Defining the Layers of the deep neural network (DNN)
+# 6 hidden layer and 60 neurons in each layer
+# Slope of 0.2 has been set for the Leaky ReLU
+# Input consist of 5 geometric parameter of all-optical plasmonic switch (AOPS) and the wavelength
+# Output is the transmission value at the wavelength (800 point for through port and 800 point for drop port)
+Model = Sequential()
+Model.add(Dense(60, input_dim=6))
+Model.add((LeakyReLU(alpha=0.2)))
+Model.add(Dropout(0.1))
+Model.add(Dense(60))
+Model.add((LeakyReLU(alpha=0.2)))
+Model.add(Dropout(0.1))
+Model.add(Dense(60))
+Model.add((LeakyReLU(alpha=0.2)))
+Model.add(Dropout(0.1))
+Model.add(Dense(60))
+Model.add((LeakyReLU(alpha=0.2)))
+Model.add(Dropout(0.1))
+Model.add(Dense(60))
+Model.add((LeakyReLU(alpha=0.2)))
+Model.add(Dropout(0.1))
+Model.add(Dense(60))
+Model.add((LeakyReLU(alpha=0.2)))
+Model.add(Dense(2))
+Model.summary()
+
+# useing EarlyStopping
+es = EarlyStopping(monitor= 'val_loss', mode = 'auto', verbose=1, patience=2,)
+
+### Configuring the settings of the training procedure 
+# Mean Squared Logarithmic Error (MSLE) function has been used for loss estimation
+# AdaDelta Optimizer has been used and learning rate of 0.1 has been set 
+Model.compile(loss='mean_squared_logarithmic_error',
+              optimizer= Adadelta(learning_rate=0.1))
+
+### Training Model 
+# batch size of 80 was set and 5000 epochs were performed
+history = Model.fit(x_train, y_train, epochs=5000,
+          batch_size = 80, validation_data=(x_val, y_val), callbacks= [es])
+
+### plot the loss graph
+plt.plot(history.history['val_loss'], linewidth=1)
+plt.plot(history.history['loss'], linewidth=2, linestyle='--')
+plt.title('The loss of training model', fontname='Times New Roman', fontsize=18, loc='center')
+plt.xlabel('epochs', fontname='Times New Roman', fontsize=18)
+plt.ylabel('loss', fontname='Times New Roman', fontsize=18)
+plt.xticks(fontfamily='Times New Roman', fontsize=14)
+plt.yticks(fontfamily='Times New Roman', fontsize=14)
+plt.legend(['train', 'Validation', 'test'])
+plt.show()
+
+### loss value of train and validation data
+train_loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+### Testing Model 
+predictions = Model.predict(x_test)
+Loss = Model.evaluate(x_test, y_test)
+print(Loss)
+
+# save the loss values in csv file
+with open('history_Forward_model.pkl', 'wb') as f:
+    pickle.dump(history.history, f)
+fieldnames = ['Epoch', 'Training Loss', 'Validation Loss']
+with open('loss_Forward_model.csv', 'w', newline='') as csvfile:
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+    writer.writeheader()
+    for epoch, train_loss_value, val_loss_value in zip(range(1, len(train_loss) + 1), train_loss, val_loss):
+        writer.writerow({'Epoch': epoch, 'Training Loss': train_loss_value, 'Validation Loss': val_loss_value})
+
+# save the forward model and its weights
+model_json = Model.to_json()
+json_file = open("T-shaped switch_Nozhat_model.json", "w")
+json_file.write(model_json)   
+Model.save_weights("T-shaped switch_Nozhat_model_weights.h5")
+json_file.close()