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+2124,89.99999806,1500,89.99999806,1500,89.99999806,1500,89.99999806,1500,89.99999806,1500,89.99999806,1500,89.99999806,1500 \ No newline at end of file diff --git a/__pycache__/autoXRD.cpython-311.pyc b/__pycache__/autoXRD.cpython-311.pyc new file mode 100644 index 0000000..ff66f6e Binary files /dev/null and b/__pycache__/autoXRD.cpython-311.pyc differ diff --git a/__pycache__/autoXRD_vis.cpython-311.pyc b/__pycache__/autoXRD_vis.cpython-311.pyc new file mode 100644 index 0000000..90cc292 Binary files /dev/null and b/__pycache__/autoXRD_vis.cpython-311.pyc differ diff --git a/__pycache__/plotting.cpython-311.pyc b/__pycache__/plotting.cpython-311.pyc new file mode 100644 index 0000000..57c7c03 Binary files /dev/null and b/__pycache__/plotting.cpython-311.pyc differ diff --git a/autoXRD_vis.py b/autoXRD_vis.py index a19c5b7..bd7347b 100644 --- a/autoXRD_vis.py +++ b/autoXRD_vis.py @@ -24,21 +24,32 @@ import numpy as np import matplotlib.pyplot as plt import pandas as pd +import os # Function to extract CAM of a certain model, subset of test elements, and spectra array with all experimental data (non-augmented) def get_cam(model_name, elements, spectra): - - model = load_model(model_name) + model = load_model(model_name) + + # Get the input layer explicitly (as before) + input_layer = model.get_layer(index=0) + + # Dummy input with expected shape (as before) + dummy_input = np.zeros((1, spectra[elements].shape[1], 1)) + _ = model(dummy_input) + gap_weights = model.layers[-1].get_weights()[0] - cam_model = Model(inputs=model.input, + + # Create the cam_model (as before) + cam_model = Model(inputs=input_layer.input, outputs=(model.layers[-3].output, model.layers[-1].output)) - features, results = cam_model.predict(spectra[elements].reshape(spectra[elements].shape[0],1200,1)) - cam_outputs=np.zeros([10,1]) - -# Extract Class Activation MAPS - for idx,element in enumerate(elements): - + # This is the key change: move cam_model.predict outside the loop + features, results = cam_model.predict(spectra[elements].reshape(spectra[elements].shape[0], 1200, 1)) + + cam_outputs = np.zeros([10, 1]) + + # Extract Class Activation MAPS + for idx, element in enumerate(elements): # get the feature map of the test image features_for_one_img = features[idx, :, :] @@ -54,37 +65,45 @@ def get_cam(model_name, elements, spectra): # create the class activation map cam_output = np.dot(cam_features, cam_weights) cam_output = abs(cam_output) - cam_outputs=np.append(cam_outputs, cam_output.reshape(cam_output.shape[0],1), axis=1) + cam_outputs = np.append(cam_outputs, cam_output.reshape(cam_output.shape[0], 1), axis=1) return cam_outputs.T # Plot CLass Activation Map of both an individual CAM and an average CAM def plot_cam(cam_output, title, *args): - #*arg is here a vector with the actual spectra - x = np.linspace(10,69.96,cam_output.shape[0]) + if cam_output.size == 0: # Check if cam_output is empty + print("Error: cam_output is empty. Unable to plot.") + return # Exit the function to prevent further errors + + x = np.linspace(10, 69.96, cam_output.shape[0]) y = cam_output - y2=[] + y2 = [] for arg in args: y2.append(arg) if len(y2) != 0: - y2=np.array(y2).reshape(1200,) + y2 = np.array(y2).reshape(1200,) - fig, (ax,ax2) = plt.subplots(nrows=2, sharex=True) + fig, (ax, ax2) = plt.subplots(nrows=2, sharex=True) - extent = [x[0]-(x[1]-x[0])/2., x[-1]+(x[1]-x[0])/2.,0,1] - ax.imshow(y[np.newaxis,:], cmap="plasma", aspect="auto", extent=extent, interpolation='gaussian') + extent = [x[0] - (x[1] - x[0]) / 2., x[-1] + (x[1] - x[0]) / 2., 0, 1] + ax.imshow(y.to_numpy()[np.newaxis, :], cmap="plasma", aspect="auto", extent=extent, interpolation='gaussian') ax.set_yticks([]) ax.set_xlim(extent[0], extent[1]) - if len(y2) != 0: - ax2.plot(np.linspace(10,69.96,1200),y2) + ax2.plot(np.linspace(10, 69.96, 1200), y2) ax2.set_xlabel(r"$2 \theta$ (Degrees)") ax2.set_title("Pattern") - fig.suptitle(title, fontsize=16) + + dirname3 = "/content/drive/MyDrive/DLP/DLP_autoXRD/plots" + + file_path = os.path.join(dirname3, title + ".png") # Customize file name + plt.savefig(file_path) + # Optional: Close the figure to release resources + plt.close() #Find corrects and incorrects in all models ran, and compare them to ground-truth def find_incorrects(ground_truth,predictions_ord): @@ -101,7 +120,7 @@ def find_incorrects(ground_truth,predictions_ord): #Join predictions and ground truth and convert to dataframe comparision_array=np.concatenate([truth,temp],axis=1) comparision_df=pd.DataFrame(data=comparision_array[:,1:], index=comparision_array[:,0], columns=['Truth','Prediction']) - comparision_df['Model']='keras_model'+str(k)+'.h5' + comparision_df['Model']='keras_model'+str(k)+'.keras' #Find incorrects and save dataframe incorrect_df=comparision_df[comparision_df.Truth != comparision_df.Prediction] @@ -113,3 +132,46 @@ def find_incorrects(ground_truth,predictions_ord): #Return list of dataframe with comparision between resuls, and list of dataframes of incorrects return corrects, incorrects + +def generate_cam(model, image, class_index): + """Generates a class activation map for a given image and class index. + + Args: + model: The trained Keras model. + image: The input image as a NumPy array. + class_index: The index of the target class. + + Returns: + A NumPy array representing the class activation map. + """ + + # Get the output of the last convolutional layer + last_conv_layer = model.get_layer('last_conv_layer') # Replace with the actual name + last_conv_layer_model = tf.keras.Model(model.inputs, last_conv_layer.output) + + # Get the weights of the classification layer + classifier_layer = model.get_layer('classifier') # Replace with the actual name + classifier_weights = classifier_layer.get_weights()[0] + + # Generate the CAM + with tf.GradientTape() as tape: + last_conv_layer_output = last_conv_layer_model(np.expand_dims(image, axis=0)) + tape.watch(last_conv_layer_output) + preds = model(np.expand_dims(image, axis=0)) + top_class_channel = preds[:, class_index] + + grads = tape.gradient(top_class_channel, last_conv_layer_output) + pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) + + heatmap = tf.reduce_mean(tf.multiply(pooled_grads, last_conv_layer_output), axis=-1) + heatmap = np.maximum(heatmap, 0) + heatmap /= np.max(heatmap) + + plt.figure() # Create a new figure to avoid overwriting previous plots + plt.imshow(image) + plt.imshow(cam, alpha=0.5, cmap='jet') + plt.axis('off') # Remove axes for a cleaner image + plt.savefig(output_path) + plt.close() + + return heatmap.numpy()[0] diff --git a/keras_model0.keras b/keras_model0.keras new file mode 100644 index 0000000..82c1b7b Binary files /dev/null and b/keras_model0.keras differ diff --git a/keras_model1.keras b/keras_model1.keras new file mode 100644 index 0000000..213f578 Binary files /dev/null and b/keras_model1.keras differ diff --git a/keras_model2.keras b/keras_model2.keras new file mode 100644 index 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a/plots/training_validation_plots_4_dropout.png b/plots/training_validation_plots_4_dropout.png new file mode 100644 index 0000000..240a6a3 Binary files /dev/null and b/plots/training_validation_plots_4_dropout.png differ diff --git a/space_group_a_CNN.py b/space_group_a_CNN.py index c0e6ee0..2f6f133 100644 --- a/space_group_a_CNN.py +++ b/space_group_a_CNN.py @@ -20,11 +20,9 @@ in the README.rst file, in the GitHub repository. """ - ################################################################# #Libraries and dependencies ################################################################# - # Loads series of functions for preprocessing and data augmentation from autoXRD import * # Loads CAMs visualizations for a-CNN @@ -37,6 +35,7 @@ from sklearn.preprocessing import OneHotEncoder from sklearn import metrics from sklearn.model_selection import KFold +from sklearn.metrics import precision_score # Neural networks uses Keran with TF background import keras as K @@ -44,12 +43,15 @@ from keras.models import Sequential from keras.callbacks import ReduceLROnPlateau from keras.callbacks import EarlyStopping +from keras.layers import GlobalAveragePooling1D +from keras.models import load_model import tensorflow as tf +from tensorflow import keras # Clear Keras and TF session, if run previously K.backend.clear_session() -tf.reset_default_graph() +#tf.reset_default_graph() # Training Parameters @@ -188,12 +190,12 @@ filter_size = 2 kernel_size = 10 - enc = OneHotEncoder(sparse=False) + enc = OneHotEncoder() train_dim = train_combine.reshape(train_combine.shape[0],1200,1) train_y = np.concatenate((y_th,exp_train_y)) trains_y.append(train_y) - train_y_hot = enc.fit_transform(train_y.reshape(-1,1)) + train_y_hot = enc.fit_transform(train_y.reshape(-1,1)).toarray() # Convert to dense array # Define network structure model = Sequential() @@ -201,7 +203,7 @@ model.add(K.layers.Conv1D(32, 8,strides=8, padding='same',input_shape=(1200,1), activation='relu')) model.add(K.layers.Conv1D(32, 5,strides=5, padding='same', activation='relu')) model.add(K.layers.Conv1D(32, 3,strides=3, padding='same', activation='relu')) - model.add(K.layers.pooling.GlobalAveragePooling1D()) + model.add(GlobalAveragePooling1D()) model.add(K.layers.Dense(n_classes, activation='softmax')) #Define optimizer @@ -211,7 +213,7 @@ model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['categorical_accuracy']) - + model.summary() # Choose early stop #early_stop = EarlyStopping(monitor='val_loss', patience=50, verbose=1, restore_best_weights=True) @@ -222,17 +224,50 @@ # Define test data test_x = X_exp[test] test_x = test_x.reshape(test_x.shape[0],1200,1) - test_y = enc.fit_transform(y_exp.reshape(-1,1))[test] - + test_y = enc.fit_transform(y_exp.reshape(-1,1)).toarray()[test] + + if isinstance(train_y_hot, tf.sparse.SparseTensor): + train_y_hot = tf.sparse.to_dense(train_y_hot) + # Fit model - hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, nb_epoch=100, + hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=50, verbose=1, validation_data=(test_x, test_y)) -# hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, nb_epoch=100, +# hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=100, # verbose=1, validation_data=(test_x, test_y), callbacks = [early_stop]) -# + + #print(hist.history.keys()) + + plt.figure(figsize=(12, 6)) + + # Loss plot + plt.subplot(1, 2, 1) + plt.plot(hist.history['loss'], label='Training Loss') + plt.plot(hist.history['val_loss'], label='Validation Loss') + plt.title('Loss over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Loss') + plt.legend() + + # Accuracy plot + plt.subplot(1, 2, 2) + plt.plot(hist.history['categorical_accuracy'], label='Training Accuracy') + plt.plot(hist.history['val_categorical_accuracy'], label='Validation Accuracy') + plt.title('Accuracy over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Accuracy') + plt.legend() + + plt.tight_layout() + + # Ensure the plot is displayed + dirname2 = "/content/drive/MyDrive/DLP/DLP_autoXRD/plots" + + plot_filename = os.path.join(dirname2, f'training_validation_plots_{k}.png') + plt.savefig(plot_filename) + #Compute model predictions prediction=model.predict(test_x) - + #Go from one-hot to ordinal... prediction_ord=[np.argmax(element) for element in prediction] predictions_ord.append(prediction_ord) @@ -241,8 +276,8 @@ accuracy_exp[k] = metrics.accuracy_score(y_exp[test], prediction_ord) accuracy_exp_r1[k] = metrics.recall_score(y_exp[test], prediction_ord, average='macro') accuracy_exp_r2[k] = metrics.recall_score(y_exp[test], prediction_ord, average='micro') - accuracy_exp_p1[k] = metrics.precision_score(y_exp[test], prediction_ord, average='macro') - accuracy_exp_p2[k] = metrics.precision_score(y_exp[test], prediction_ord, average='micro') + accuracy_exp_p1[k] = metrics.precision_score(y_exp[test], prediction_ord, average='macro', zero_division=1) + accuracy_exp_p2[k] = metrics.precision_score(y_exp[test], prediction_ord, average='micro', zero_division=1) f1[k]=metrics.f1_score(y_exp[test], prediction_ord, average='micro') f1_m[k]=metrics.f1_score(y_exp[test], prediction_ord, average='macro') @@ -257,7 +292,8 @@ logs.append(log) #Save models on current folder with names subscripts 0 to 4 - model.save(os.path.join(dirname, 'keras_model')+str(k)+'.h5') + #model.save(os.path.join(dirname, 'keras_model')+str(k)+'.h5') + model.save(os.path.join(dirname, 'keras_model') + str(k) + '.keras') # accuracy = np.array(accuracy) @@ -272,6 +308,13 @@ # Compute correctly classified and incorrectly classified cases corrects, incorrects = find_incorrects(ground_truth,predictions_ord) +#def get_cam(model_path, trains, X_exp): + # Load the model using keras.models.load_model +# model = keras.models.load_model(model_path, compile=False) + + # Compile the loaded model, ensure it's ready for use +# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + # Get dataframe of all incorrects and dataframe of all corrects corrects_df = pd.concat([r for r in corrects], ignore_index=False, axis=0) incorrects_df = pd.concat([r for r in incorrects], ignore_index=False, axis=0) @@ -280,23 +323,24 @@ # Trains refers to the elements in X_exp used for training # Choose the model in cross validation as output, in this case we plot number 5 -cam_outputs=get_cam('keras_model4.h5', trains[4], X_exp) +cam_outputs=get_cam('keras_model4.keras', trains[4], X_exp) +#print(f"this is the cam_outputs",cam_outputs) cam_df=pd.DataFrame(cam_outputs) cam_df=cam_df.iloc[1:] cam_df['Label']=y_exp[trains[4]] # CAM with all augmented training data rng=range(0,7000) -cam_outputs2=get_cam('keras_model4.h5', rng, train_combine) +cam_outputs2=get_cam('keras_model4.keras', rng, train_combine) cam_df2=pd.DataFrame(cam_outputs2) cam_df2=cam_df2.iloc[1:] cam_df2['Label']=train_y # Now we focus on the incorrectly labelled cam's -incorrects_filtered=incorrects_df[incorrects_df.Model=='keras_model4.h5'] +incorrects_filtered=incorrects_df[incorrects_df.Model=='keras_model4.keras'] # Get CAM of incorrectly classified cases -cam_inc=get_cam('keras_model4.h5', [int(element) for element in incorrects_filtered.index], X_exp) +cam_inc=get_cam('keras_model4.keras', [int(element) for element in incorrects_filtered.index], X_exp) cam_inc=pd.DataFrame(cam_inc) cam_inc=cam_inc.iloc[1:] @@ -309,12 +353,14 @@ means_6=cam_filtered.mean() means_6=means_6.iloc[:-1] +#print(f"this is the input to the plot_cam",means_6) #check if the input is empty + #Plot the average class map for class 6 plot_cam(means_6,'Average CAM for Class 6, trained model4.h5') #Plot correctly classified CAMs and patterns, no need to change this -corrects_filtered=corrects_df[corrects_df.Model=='keras_model4.h5'] -cam_cor=get_cam('keras_model4.h5', [int(element) for element in corrects_filtered.index], X_exp) +corrects_filtered=corrects_df[corrects_df.Model=='keras_model4.keras'] +cam_cor=get_cam('keras_model4.keras', [int(element) for element in corrects_filtered.index], X_exp) cam_cor=pd.DataFrame(cam_cor) cam_cor=cam_cor.iloc[1:] diff --git a/space_group_a_CNN_L1.py b/space_group_a_CNN_L1.py new file mode 100644 index 0000000..c180c4f --- /dev/null +++ b/space_group_a_CNN_L1.py @@ -0,0 +1,374 @@ +#-*- coding: utf-8 -*- +#!usr/bin/env python + +""" +SPACE GROUP a-CNN + +filneame: space_group_a_CNN.py version: 1.0 +dependencies: + autoXRD version 1.0 + autoXRD_vis version 0.2 + +Code to perform classification of XRD patterns for various spcae-group using +physics-informed data augmentation and all convolutional neural network (a-CNN). +Code to plot class activation maps from a-CNN and global average pooling layer + +@authors: Felipe Oviedo and Danny Zekun Ren +MIT Photovoltaics Laboratory / Singapore and MIT Alliance for Research and Tehcnology + +All code is under Apache 2.0 license, please cite any use of the code as explained +in the README.rst file, in the GitHub repository. + +""" +################################################################# +#Libraries and dependencies +################################################################# +# Loads series of functions for preprocessing and data augmentation +from autoXRD import * +# Loads CAMs visualizations for a-CNN +from autoXRD_vis import * + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import OneHotEncoder +from sklearn import metrics +from sklearn.model_selection import KFold +from sklearn.metrics import precision_score + +# Neural networks uses Keran with TF background +import keras as K +from keras.models import Model +from keras.models import Sequential +from keras.callbacks import ReduceLROnPlateau +from keras.callbacks import EarlyStopping +from keras.layers import GlobalAveragePooling1D +from keras.models import load_model + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import regularizers + +# Clear Keras and TF session, if run previously +K.backend.clear_session() +#tf.reset_default_graph() + +# Training Parameters + +BATCH_SIZE=128 + +# Network Parameters +n_input = 1200 # Total angles in XRD pattern +n_classes = 7 # Number of space-group classes +filter_size = 2 +kernel_size = 10 + +################################################################ +# Load data and preprocess +################################################################ + +# Load simulated and anonimized dataset +import os +dirname = os.path.dirname(__file__) + +theor = pd.read_csv(os.path.join(dirname, 'Datasets/theor.csv'), index_col=0) +theor = theor.iloc[1:,] +theor_arr=theor.values + +# Normalize data for training +ntheor = normdata(theor_arr) + +# Load labels for simulated data +label_theo = pd.read_csv(os.path.join(dirname, 'Datasets/label_theo.csv'), header=None, index_col=0) +label_theo = label_theo[1].tolist() + +# Load experimental data as dataframe +exp_arr_new = pd.read_csv(os.path.join(dirname, 'Datasets/exp.csv'), index_col=0) +exp_arr_new = exp_arr_new.values + +# Load experimental class labels +label_exp= pd.read_csv(os.path.join(dirname, 'Datasets/label_exp.csv'), index_col=0).values +label_exp = label_exp.reshape([len(label_exp),]) + +# Load class enconding +space_group_enc = pd.read_csv(os.path.join(dirname, 'Datasets/encoding.csv'), index_col=0) +space_group_enc = list(space_group_enc['0']) + +# Normalize experimental data +nexp = normdata(exp_arr_new) + +# Define spectral range for data augmentation +exp_min = 0 +exp_max = 1200 +theor_min = 125 + +#window size for experimental data extraction +window = 20 +theor_max = theor_min+exp_max-exp_min + +# Preprocess experimental data +post_exp = normdatasingle(exp_data_processing (nexp, exp_min, exp_max, window)) + +################################################################ +# Perform data augmentation +################################################################ + +# Specify how many data points we augmented +th_num = 2000 + +# Augment data, this may take a bit +augd,pard,crop_augd = augdata(ntheor, th_num, label_theo, theor_min, theor_max) + +# Enconde theoretical labels +label_t=np.zeros([len(pard),]) +for i in range(len(pard)): + label_t[i]=space_group_enc.index(pard[i]) + +# Input the num of experimetal data points +exp_num =88 + +# Prepare experimental arrays for training and testing +X_exp = np.transpose(post_exp[:,0:exp_num]) +y_exp = label_exp[0:exp_num] + +# Prepare simulated arrays for training and testing +X_th = np.transpose(crop_augd ) +y_th = label_t + +################################################################ +# Perform training and cross-validation +################################################################ + +fold = 5 # Number of k-folds + +k_fold = KFold(n_splits=fold, shuffle=True, random_state=3) + +# Create arrays to populate metrics +accuracy_exp = np.empty((fold,1)) +accuracy_exp_b = np.empty((fold,1)) +accuracy_exp_r1 = np.empty((fold,1)) +accuracy_exp_p1 = np.empty((fold,1)) +accuracy_exp_r2 = np.empty((fold,1)) +accuracy_exp_p2 = np.empty((fold,1)) +f1=np.empty((fold,1)) +f1_m=np.empty((fold,1)) + +# Create auxiliary arrays +accuracy=[] +logs=[] +ground_truth=[] +predictions_ord=[] +trains=[] +tests=[] +trains_combine=[] +trains_y=[] + +# Run cross validation and define a-CNN each time in loop +for k, (train, test) in enumerate(k_fold.split(X_exp, y_exp)): + + #Save splits for later use + trains.append(train) + tests.append(test) + + #Data augmentation of experimental traning dataset, we + # already removed the experimental training dataset + temp_x = X_exp[train] + temp_y = y_exp[train] + exp_train_x,exp_train_y = exp_augdata(temp_x.T,5000,temp_y) + + # Combine theoretical and experimenal dataset for training + train_combine = np.concatenate((X_th,exp_train_x.T)) + trains_combine.append(train_combine) + + # Clear weights and networks state + K.backend.clear_session() + + # Network Parameters + BATCH_SIZE=128 + n_input = 1200 # MNIST data input (img shape: 28*28) + n_classes = 7 # MNIST total classes (0-9 digits) + filter_size = 2 + kernel_size = 10 + + enc = OneHotEncoder() + + train_dim = train_combine.reshape(train_combine.shape[0],1200,1) + train_y = np.concatenate((y_th,exp_train_y)) + trains_y.append(train_y) + train_y_hot = enc.fit_transform(train_y.reshape(-1,1)).toarray() # Convert to dense array + + # Define network structure + model = Sequential() + + model.add(K.layers.Conv1D(32, 8,strides=8, padding='same',input_shape=(1200,1), activation='relu', kernel_regularizer=regularizers.l1(0.01))) + model.add(K.layers.Conv1D(32, 5,strides=5, padding='same', activation='relu',kernel_regularizer=regularizers.l1(0.01))) + model.add(K.layers.Conv1D(32, 3,strides=3, padding='same', activation='relu',kernel_regularizer=regularizers.l1(0.01))) + model.add(GlobalAveragePooling1D()) + model.add(K.layers.Dense(n_classes, activation='softmax')) + + #Define optimizer + optimizer = K.optimizers.Adam() + + # Compile model + model.compile(loss='binary_crossentropy', + optimizer=optimizer, + metrics=['categorical_accuracy']) + model.summary() + # Choose early stop + #early_stop = EarlyStopping(monitor='val_loss', patience=50, verbose=1, restore_best_weights=True) + + # Reduce learning rate during optimization +# reduce_lr = ReduceLROnPlateau(monitor = 'loss', factor=0.5, +# patience=50, min_lr=0.00001) + + # Define test data + test_x = X_exp[test] + test_x = test_x.reshape(test_x.shape[0],1200,1) + test_y = enc.fit_transform(y_exp.reshape(-1,1)).toarray()[test] + + if isinstance(train_y_hot, tf.sparse.SparseTensor): + train_y_hot = tf.sparse.to_dense(train_y_hot) + + # Fit model + hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=50, + verbose=1, validation_data=(test_x, test_y)) +# hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=100, +# verbose=1, validation_data=(test_x, test_y), callbacks = [early_stop]) + + #print(hist.history.keys()) + + plt.figure(figsize=(12, 6)) + + # Loss plot + plt.subplot(1, 2, 1) + plt.plot(hist.history['loss'], label='Training Loss') + plt.plot(hist.history['val_loss'], label='Validation Loss') + plt.title('Loss over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Loss') + plt.legend() + + # Accuracy plot + plt.subplot(1, 2, 2) + plt.plot(hist.history['categorical_accuracy'], label='Training Accuracy') + plt.plot(hist.history['val_categorical_accuracy'], label='Validation Accuracy') + plt.title('Accuracy over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Accuracy') + plt.legend() + + plt.tight_layout() + + # Ensure the plot is displayed + dirname2 = "/content/drive/MyDrive/DLP/DLP_autoXRD/plots" + + plot_filename = os.path.join(dirname2, f'training_validation_plots_{k}_L1.png') + plt.savefig(plot_filename) + + #Compute model predictions + prediction=model.predict(test_x) + + #Go from one-hot to ordinal... + prediction_ord=[np.argmax(element) for element in prediction] + predictions_ord.append(prediction_ord) + + # Compute accuracy, recall, precision and F1 with macro and micro averaging + accuracy_exp[k] = metrics.accuracy_score(y_exp[test], prediction_ord) + accuracy_exp_r1[k] = metrics.recall_score(y_exp[test], prediction_ord, average='macro') + accuracy_exp_r2[k] = metrics.recall_score(y_exp[test], prediction_ord, average='micro') + accuracy_exp_p1[k] = metrics.precision_score(y_exp[test], prediction_ord, average='macro', zero_division=1) + accuracy_exp_p2[k] = metrics.precision_score(y_exp[test], prediction_ord, average='micro', zero_division=1) + f1[k]=metrics.f1_score(y_exp[test], prediction_ord, average='micro') + f1_m[k]=metrics.f1_score(y_exp[test], prediction_ord, average='macro') + + #Produce ground_truth, each list element contains array with test elements on first column with respect to X_exp and + ground_truth.append(np.concatenate([test.reshape(len(test),1),y_exp[test].reshape(len(test),1)],axis=1)) + + #Compute loss and accuracy for each k validation + accuracy.append(model.evaluate(test_x, test_y, verbose=0)) + + #Save logs + log = pd.DataFrame(hist.history) + logs.append(log) + + #Save models on current folder with names subscripts 0 to 4 + #model.save(os.path.join(dirname, 'keras_model')+str(k)+'.h5') + model.save(os.path.join(dirname, 'keras_model') + str(k) + '.keras') + +# +accuracy = np.array(accuracy) + +# Plot final cross validation accuracy +print ('Mean Cross-val accuracy', np.mean(accuracy[:,1])) + +################################################################ +# Plotting Class Activation Maps +################################################################ + +# Compute correctly classified and incorrectly classified cases +corrects, incorrects = find_incorrects(ground_truth,predictions_ord) + +#def get_cam(model_path, trains, X_exp): + # Load the model using keras.models.load_model +# model = keras.models.load_model(model_path, compile=False) + + # Compile the loaded model, ensure it's ready for use +# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + +# Get dataframe of all incorrects and dataframe of all corrects +corrects_df = pd.concat([r for r in corrects], ignore_index=False, axis=0) +incorrects_df = pd.concat([r for r in incorrects], ignore_index=False, axis=0) + +# Get the cam for the trained examples, for each class we average the cam of all trained examples +# Trains refers to the elements in X_exp used for training +# Choose the model in cross validation as output, in this case we plot number 5 + +cam_outputs=get_cam('keras_model4.keras', trains[4], X_exp) +#print(f"this is the cam_outputs",cam_outputs) +cam_df=pd.DataFrame(cam_outputs) +cam_df=cam_df.iloc[1:] +cam_df['Label']=y_exp[trains[4]] + +# CAM with all augmented training data +rng=range(0,7000) +cam_outputs2=get_cam('keras_model4.keras', rng, train_combine) +cam_df2=pd.DataFrame(cam_outputs2) +cam_df2=cam_df2.iloc[1:] +cam_df2['Label']=train_y + +# Now we focus on the incorrectly labelled cam's +incorrects_filtered=incorrects_df[incorrects_df.Model=='keras_model4.keras'] + +# Get CAM of incorrectly classified cases +cam_inc=get_cam('keras_model4.keras', [int(element) for element in incorrects_filtered.index], X_exp) +cam_inc=pd.DataFrame(cam_inc) +cam_inc=cam_inc.iloc[1:] + +# Redefine main variable +cam_df=cam_df2 + +#Get the average class maps for a certain class, in this case 6 + +cam_filtered=cam_df[cam_df.Label==6] +means_6=cam_filtered.mean() +means_6=means_6.iloc[:-1] + +#print(f"this is the input to the plot_cam",means_6) #check if the input is empty + +#Plot the average class map for class 6 +plot_cam(means_6,'Average CAM for Class 6 with L1, trained model4.kears') + +#Plot correctly classified CAMs and patterns, no need to change this +corrects_filtered=corrects_df[corrects_df.Model=='keras_model4.keras'] +cam_cor=get_cam('keras_model4.keras', [int(element) for element in corrects_filtered.index], X_exp) +cam_cor=pd.DataFrame(cam_cor) +cam_cor=cam_cor.iloc[1:] + +#Plot a single correctly classified pattern +plot_cam(cam_cor.iloc[-1,:],'CAM for single pattern: true class 6 with L1, predicted is 6', X_exp[int(corrects_filtered.index[-1])]) + +# Note that results may vary due to NN variability during training, make sure you are plotting +# correct and incorrect cases clearly + +################################################################ \ No newline at end of file diff --git a/space_group_a_CNN_batch_norm.py b/space_group_a_CNN_batch_norm.py new file mode 100644 index 0000000..9c544b5 --- /dev/null +++ b/space_group_a_CNN_batch_norm.py @@ -0,0 +1,376 @@ +#-*- coding: utf-8 -*- +#!usr/bin/env python + +""" +SPACE GROUP a-CNN + +filneame: space_group_a_CNN.py version: 1.0 +dependencies: + autoXRD version 1.0 + autoXRD_vis version 0.2 + +Code to perform classification of XRD patterns for various spcae-group using +physics-informed data augmentation and all convolutional neural network (a-CNN). +Code to plot class activation maps from a-CNN and global average pooling layer + +@authors: Felipe Oviedo and Danny Zekun Ren +MIT Photovoltaics Laboratory / Singapore and MIT Alliance for Research and Tehcnology + +All code is under Apache 2.0 license, please cite any use of the code as explained +in the README.rst file, in the GitHub repository. + +""" +################################################################# +#Libraries and dependencies +################################################################# +# Loads series of functions for preprocessing and data augmentation +from autoXRD import * +# Loads CAMs visualizations for a-CNN +from autoXRD_vis import * + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import OneHotEncoder +from sklearn import metrics +from sklearn.model_selection import KFold +from sklearn.metrics import precision_score + +# Neural networks uses Keran with TF background +import keras as K +from keras.models import Model +from keras.models import Sequential +from keras.callbacks import ReduceLROnPlateau +from keras.callbacks import EarlyStopping +from keras.layers import GlobalAveragePooling1D +from keras.models import load_model + +import tensorflow as tf +from tensorflow import keras + +# Clear Keras and TF session, if run previously +K.backend.clear_session() +#tf.reset_default_graph() + +# Training Parameters + +BATCH_SIZE=128 + +# Network Parameters +n_input = 1200 # Total angles in XRD pattern +n_classes = 7 # Number of space-group classes +filter_size = 2 +kernel_size = 10 + +################################################################ +# Load data and preprocess +################################################################ + +# Load simulated and anonimized dataset +import os +dirname = os.path.dirname(__file__) + +theor = pd.read_csv(os.path.join(dirname, 'Datasets/theor.csv'), index_col=0) +theor = theor.iloc[1:,] +theor_arr=theor.values + +# Normalize data for training +ntheor = normdata(theor_arr) + +# Load labels for simulated data +label_theo = pd.read_csv(os.path.join(dirname, 'Datasets/label_theo.csv'), header=None, index_col=0) +label_theo = label_theo[1].tolist() + +# Load experimental data as dataframe +exp_arr_new = pd.read_csv(os.path.join(dirname, 'Datasets/exp.csv'), index_col=0) +exp_arr_new = exp_arr_new.values + +# Load experimental class labels +label_exp= pd.read_csv(os.path.join(dirname, 'Datasets/label_exp.csv'), index_col=0).values +label_exp = label_exp.reshape([len(label_exp),]) + +# Load class enconding +space_group_enc = pd.read_csv(os.path.join(dirname, 'Datasets/encoding.csv'), index_col=0) +space_group_enc = list(space_group_enc['0']) + +# Normalize experimental data +nexp = normdata(exp_arr_new) + +# Define spectral range for data augmentation +exp_min = 0 +exp_max = 1200 +theor_min = 125 + +#window size for experimental data extraction +window = 20 +theor_max = theor_min+exp_max-exp_min + +# Preprocess experimental data +post_exp = normdatasingle(exp_data_processing (nexp, exp_min, exp_max, window)) + +################################################################ +# Perform data augmentation +################################################################ + +# Specify how many data points we augmented +th_num = 2000 + +# Augment data, this may take a bit +augd,pard,crop_augd = augdata(ntheor, th_num, label_theo, theor_min, theor_max) + +# Enconde theoretical labels +label_t=np.zeros([len(pard),]) +for i in range(len(pard)): + label_t[i]=space_group_enc.index(pard[i]) + +# Input the num of experimetal data points +exp_num =88 + +# Prepare experimental arrays for training and testing +X_exp = np.transpose(post_exp[:,0:exp_num]) +y_exp = label_exp[0:exp_num] + +# Prepare simulated arrays for training and testing +X_th = np.transpose(crop_augd ) +y_th = label_t + +################################################################ +# Perform training and cross-validation +################################################################ + +fold = 5 # Number of k-folds + +k_fold = KFold(n_splits=fold, shuffle=True, random_state=3) + +# Create arrays to populate metrics +accuracy_exp = np.empty((fold,1)) +accuracy_exp_b = np.empty((fold,1)) +accuracy_exp_r1 = np.empty((fold,1)) +accuracy_exp_p1 = np.empty((fold,1)) +accuracy_exp_r2 = np.empty((fold,1)) +accuracy_exp_p2 = np.empty((fold,1)) +f1=np.empty((fold,1)) +f1_m=np.empty((fold,1)) + +# Create auxiliary arrays +accuracy=[] +logs=[] +ground_truth=[] +predictions_ord=[] +trains=[] +tests=[] +trains_combine=[] +trains_y=[] + +# Run cross validation and define a-CNN each time in loop +for k, (train, test) in enumerate(k_fold.split(X_exp, y_exp)): + + #Save splits for later use + trains.append(train) + tests.append(test) + + #Data augmentation of experimental traning dataset, we + # already removed the experimental training dataset + temp_x = X_exp[train] + temp_y = y_exp[train] + exp_train_x,exp_train_y = exp_augdata(temp_x.T,5000,temp_y) + + # Combine theoretical and experimenal dataset for training + train_combine = np.concatenate((X_th,exp_train_x.T)) + trains_combine.append(train_combine) + + # Clear weights and networks state + K.backend.clear_session() + + # Network Parameters + BATCH_SIZE=128 + n_input = 1200 # MNIST data input (img shape: 28*28) + n_classes = 7 # MNIST total classes (0-9 digits) + filter_size = 2 + kernel_size = 10 + + enc = OneHotEncoder() + + train_dim = train_combine.reshape(train_combine.shape[0],1200,1) + train_y = np.concatenate((y_th,exp_train_y)) + trains_y.append(train_y) + train_y_hot = enc.fit_transform(train_y.reshape(-1,1)).toarray() # Convert to dense array + + # Define network structure + model = Sequential() + + model.add(K.layers.Conv1D(32, 8,strides=8, padding='same',input_shape=(1200,1), activation='relu')) + model.add(K.layers.BatchNormalization()) + model.add(K.layers.Conv1D(32, 5,strides=5, padding='same', activation='relu')) + model.add(K.layers.BatchNormalization()) + model.add(K.layers.Conv1D(32, 3,strides=3, padding='same', activation='relu')) + model.add(K.layers.BatchNormalization()) + model.add(GlobalAveragePooling1D()) + model.add(K.layers.Dense(n_classes, activation='softmax')) + + #Define optimizer + optimizer = K.optimizers.Adam() + + # Compile model + model.compile(loss='binary_crossentropy', + optimizer=optimizer, + metrics=['categorical_accuracy']) + model.summary() + # Choose early stop + #early_stop = EarlyStopping(monitor='val_loss', patience=50, verbose=1, restore_best_weights=True) + + # Reduce learning rate during optimization +# reduce_lr = ReduceLROnPlateau(monitor = 'loss', factor=0.5, +# patience=50, min_lr=0.00001) + + # Define test data + test_x = X_exp[test] + test_x = test_x.reshape(test_x.shape[0],1200,1) + test_y = enc.fit_transform(y_exp.reshape(-1,1)).toarray()[test] + + if isinstance(train_y_hot, tf.sparse.SparseTensor): + train_y_hot = tf.sparse.to_dense(train_y_hot) + + # Fit model + hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=50, + verbose=1, validation_data=(test_x, test_y)) +# hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=100, +# verbose=1, validation_data=(test_x, test_y), callbacks = [early_stop]) + + #print(hist.history.keys()) + + plt.figure(figsize=(12, 6)) + + # Loss plot + plt.subplot(1, 2, 1) + plt.plot(hist.history['loss'], label='Training Loss') + plt.plot(hist.history['val_loss'], label='Validation Loss') + plt.title('Loss over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Loss') + plt.legend() + + # Accuracy plot + plt.subplot(1, 2, 2) + plt.plot(hist.history['categorical_accuracy'], label='Training Accuracy') + plt.plot(hist.history['val_categorical_accuracy'], label='Validation Accuracy') + plt.title('Accuracy over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Accuracy') + plt.legend() + + plt.tight_layout() + + # Ensure the plot is displayed + dirname2 = "/content/drive/MyDrive/DLP/DLP_autoXRD/plots" + + plot_filename = os.path.join(dirname2, f'training_validation_plots_{k}_batch_norm.png') + plt.savefig(plot_filename) + + #Compute model predictions + prediction=model.predict(test_x) + + #Go from one-hot to ordinal... + prediction_ord=[np.argmax(element) for element in prediction] + predictions_ord.append(prediction_ord) + + # Compute accuracy, recall, precision and F1 with macro and micro averaging + accuracy_exp[k] = metrics.accuracy_score(y_exp[test], prediction_ord) + accuracy_exp_r1[k] = metrics.recall_score(y_exp[test], prediction_ord, average='macro') + accuracy_exp_r2[k] = metrics.recall_score(y_exp[test], prediction_ord, average='micro') + accuracy_exp_p1[k] = metrics.precision_score(y_exp[test], prediction_ord, average='macro', zero_division=1) + accuracy_exp_p2[k] = metrics.precision_score(y_exp[test], prediction_ord, average='micro', zero_division=1) + f1[k]=metrics.f1_score(y_exp[test], prediction_ord, average='micro') + f1_m[k]=metrics.f1_score(y_exp[test], prediction_ord, average='macro') + + #Produce ground_truth, each list element contains array with test elements on first column with respect to X_exp and + ground_truth.append(np.concatenate([test.reshape(len(test),1),y_exp[test].reshape(len(test),1)],axis=1)) + + #Compute loss and accuracy for each k validation + accuracy.append(model.evaluate(test_x, test_y, verbose=0)) + + #Save logs + log = pd.DataFrame(hist.history) + logs.append(log) + + #Save models on current folder with names subscripts 0 to 4 + #model.save(os.path.join(dirname, 'keras_model')+str(k)+'.h5') + model.save(os.path.join(dirname, 'keras_model_batch_norm') + str(k) + '.keras') + +# +accuracy = np.array(accuracy) + +# Plot final cross validation accuracy +print ('Mean Cross-val accuracy', np.mean(accuracy[:,1])) + +################################################################ +# Plotting Class Activation Maps +################################################################ + +# Compute correctly classified and incorrectly classified cases +corrects, incorrects = find_incorrects(ground_truth,predictions_ord) + +#def get_cam(model_path, trains, X_exp): + # Load the model using keras.models.load_model +# model = keras.models.load_model(model_path, compile=False) + + # Compile the loaded model, ensure it's ready for use +# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + +# Get dataframe of all incorrects and dataframe of all corrects +corrects_df = pd.concat([r for r in corrects], ignore_index=False, axis=0) +incorrects_df = pd.concat([r for r in incorrects], ignore_index=False, axis=0) + +# Get the cam for the trained examples, for each class we average the cam of all trained examples +# Trains refers to the elements in X_exp used for training +# Choose the model in cross validation as output, in this case we plot number 5 + +cam_outputs=get_cam('keras_model4.keras', trains[4], X_exp) +#print(f"this is the cam_outputs",cam_outputs) +cam_df=pd.DataFrame(cam_outputs) +cam_df=cam_df.iloc[1:] +cam_df['Label']=y_exp[trains[4]] + +# CAM with all augmented training data +rng=range(0,7000) +cam_outputs2=get_cam('keras_model4.keras', rng, train_combine) +cam_df2=pd.DataFrame(cam_outputs2) +cam_df2=cam_df2.iloc[1:] +cam_df2['Label']=train_y + +# Now we focus on the incorrectly labelled cam's +incorrects_filtered=incorrects_df[incorrects_df.Model=='keras_model4.keras'] + +# Get CAM of incorrectly classified cases +cam_inc=get_cam('keras_model4.keras', [int(element) for element in incorrects_filtered.index], X_exp) +cam_inc=pd.DataFrame(cam_inc) +cam_inc=cam_inc.iloc[1:] + +# Redefine main variable +cam_df=cam_df2 + +#Get the average class maps for a certain class, in this case 6 + +cam_filtered=cam_df[cam_df.Label==6] +means_6=cam_filtered.mean() +means_6=means_6.iloc[:-1] + +#print(f"this is the input to the plot_cam",means_6) #check if the input is empty + +#Plot the average class map for class 6 +plot_cam(means_6,'Average CAM for Class 6 with batch norm, trained model4.keras') + +#Plot correctly classified CAMs and patterns, no need to change this +corrects_filtered=corrects_df[corrects_df.Model=='keras_model4.keras'] +cam_cor=get_cam('keras_model4.keras', [int(element) for element in corrects_filtered.index], X_exp) +cam_cor=pd.DataFrame(cam_cor) +cam_cor=cam_cor.iloc[1:] + +#Plot a single correctly classified pattern +plot_cam(cam_cor.iloc[-1,:],'CAM for single pattern with batch norm: true class 6, predicted is 6', X_exp[int(corrects_filtered.index[-1])]) + +# Note that results may vary due to NN variability during training, make sure you are plotting +# correct and incorrect cases clearly + +################################################################ \ No newline at end of file diff --git a/space_group_a_CNN_drop_out.py b/space_group_a_CNN_drop_out.py new file mode 100644 index 0000000..f5739ff --- /dev/null +++ b/space_group_a_CNN_drop_out.py @@ -0,0 +1,376 @@ +#-*- coding: utf-8 -*- +#!usr/bin/env python + +""" +SPACE GROUP a-CNN + +filneame: space_group_a_CNN.py version: 1.0 +dependencies: + autoXRD version 1.0 + autoXRD_vis version 0.2 + +Code to perform classification of XRD patterns for various spcae-group using +physics-informed data augmentation and all convolutional neural network (a-CNN). +Code to plot class activation maps from a-CNN and global average pooling layer + +@authors: Felipe Oviedo and Danny Zekun Ren +MIT Photovoltaics Laboratory / Singapore and MIT Alliance for Research and Tehcnology + +All code is under Apache 2.0 license, please cite any use of the code as explained +in the README.rst file, in the GitHub repository. + +""" +################################################################# +#Libraries and dependencies +################################################################# +# Loads series of functions for preprocessing and data augmentation +from autoXRD import * +# Loads CAMs visualizations for a-CNN +from autoXRD_vis import * + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import OneHotEncoder +from sklearn import metrics +from sklearn.model_selection import KFold +from sklearn.metrics import precision_score + +# Neural networks uses Keran with TF background +import keras as K +from keras.models import Model +from keras.models import Sequential +from keras.callbacks import ReduceLROnPlateau +from keras.callbacks import EarlyStopping +from keras.layers import GlobalAveragePooling1D +from keras.models import load_model + +import tensorflow as tf +from tensorflow import keras + +# Clear Keras and TF session, if run previously +K.backend.clear_session() +#tf.reset_default_graph() + +# Training Parameters + +BATCH_SIZE=128 + +# Network Parameters +n_input = 1200 # Total angles in XRD pattern +n_classes = 7 # Number of space-group classes +filter_size = 2 +kernel_size = 10 + +################################################################ +# Load data and preprocess +################################################################ + +# Load simulated and anonimized dataset +import os +dirname = os.path.dirname(__file__) + +theor = pd.read_csv(os.path.join(dirname, 'Datasets/theor.csv'), index_col=0) +theor = theor.iloc[1:,] +theor_arr=theor.values + +# Normalize data for training +ntheor = normdata(theor_arr) + +# Load labels for simulated data +label_theo = pd.read_csv(os.path.join(dirname, 'Datasets/label_theo.csv'), header=None, index_col=0) +label_theo = label_theo[1].tolist() + +# Load experimental data as dataframe +exp_arr_new = pd.read_csv(os.path.join(dirname, 'Datasets/exp.csv'), index_col=0) +exp_arr_new = exp_arr_new.values + +# Load experimental class labels +label_exp= pd.read_csv(os.path.join(dirname, 'Datasets/label_exp.csv'), index_col=0).values +label_exp = label_exp.reshape([len(label_exp),]) + +# Load class enconding +space_group_enc = pd.read_csv(os.path.join(dirname, 'Datasets/encoding.csv'), index_col=0) +space_group_enc = list(space_group_enc['0']) + +# Normalize experimental data +nexp = normdata(exp_arr_new) + +# Define spectral range for data augmentation +exp_min = 0 +exp_max = 1200 +theor_min = 125 + +#window size for experimental data extraction +window = 20 +theor_max = theor_min+exp_max-exp_min + +# Preprocess experimental data +post_exp = normdatasingle(exp_data_processing (nexp, exp_min, exp_max, window)) + +################################################################ +# Perform data augmentation +################################################################ + +# Specify how many data points we augmented +th_num = 2000 + +# Augment data, this may take a bit +augd,pard,crop_augd = augdata(ntheor, th_num, label_theo, theor_min, theor_max) + +# Enconde theoretical labels +label_t=np.zeros([len(pard),]) +for i in range(len(pard)): + label_t[i]=space_group_enc.index(pard[i]) + +# Input the num of experimetal data points +exp_num =88 + +# Prepare experimental arrays for training and testing +X_exp = np.transpose(post_exp[:,0:exp_num]) +y_exp = label_exp[0:exp_num] + +# Prepare simulated arrays for training and testing +X_th = np.transpose(crop_augd ) +y_th = label_t + +################################################################ +# Perform training and cross-validation +################################################################ + +fold = 5 # Number of k-folds + +k_fold = KFold(n_splits=fold, shuffle=True, random_state=3) + +# Create arrays to populate metrics +accuracy_exp = np.empty((fold,1)) +accuracy_exp_b = np.empty((fold,1)) +accuracy_exp_r1 = np.empty((fold,1)) +accuracy_exp_p1 = np.empty((fold,1)) +accuracy_exp_r2 = np.empty((fold,1)) +accuracy_exp_p2 = np.empty((fold,1)) +f1=np.empty((fold,1)) +f1_m=np.empty((fold,1)) + +# Create auxiliary arrays +accuracy=[] +logs=[] +ground_truth=[] +predictions_ord=[] +trains=[] +tests=[] +trains_combine=[] +trains_y=[] + +# Run cross validation and define a-CNN each time in loop +for k, (train, test) in enumerate(k_fold.split(X_exp, y_exp)): + + #Save splits for later use + trains.append(train) + tests.append(test) + + #Data augmentation of experimental traning dataset, we + # already removed the experimental training dataset + temp_x = X_exp[train] + temp_y = y_exp[train] + exp_train_x,exp_train_y = exp_augdata(temp_x.T,5000,temp_y) + + # Combine theoretical and experimenal dataset for training + train_combine = np.concatenate((X_th,exp_train_x.T)) + trains_combine.append(train_combine) + + # Clear weights and networks state + K.backend.clear_session() + + # Network Parameters + BATCH_SIZE=128 + n_input = 1200 # MNIST data input (img shape: 28*28) + n_classes = 7 # MNIST total classes (0-9 digits) + filter_size = 2 + kernel_size = 10 + + enc = OneHotEncoder() + + train_dim = train_combine.reshape(train_combine.shape[0],1200,1) + train_y = np.concatenate((y_th,exp_train_y)) + trains_y.append(train_y) + train_y_hot = enc.fit_transform(train_y.reshape(-1,1)).toarray() # Convert to dense array + + # Define network structure + model = Sequential() + + model.add(K.layers.Conv1D(32, 8,strides=8, padding='same',input_shape=(1200,1), activation='relu')) + model.add(K.layers.Dropout(0.25)) # Dropout after the first convolutional layer + model.add(K.layers.Conv1D(32, 5,strides=5, padding='same', activation='relu')) + model.add(K.layers.Dropout(0.25)) # Dropout after the second convolutional layer + model.add(K.layers.Conv1D(32, 3,strides=3, padding='same', activation='relu')) + model.add(K.layers.Dropout(0.25)) # Dropout after the third convolutional layer + model.add(GlobalAveragePooling1D()) + model.add(K.layers.Dense(n_classes, activation='softmax')) + + #Define optimizer + optimizer = K.optimizers.Adam() + + # Compile model + model.compile(loss='binary_crossentropy', + optimizer=optimizer, + metrics=['categorical_accuracy']) + model.summary() + # Choose early stop + #early_stop = EarlyStopping(monitor='val_loss', patience=50, verbose=1, restore_best_weights=True) + + # Reduce learning rate during optimization +# reduce_lr = ReduceLROnPlateau(monitor = 'loss', factor=0.5, +# patience=50, min_lr=0.00001) + + # Define test data + test_x = X_exp[test] + test_x = test_x.reshape(test_x.shape[0],1200,1) + test_y = enc.fit_transform(y_exp.reshape(-1,1)).toarray()[test] + + if isinstance(train_y_hot, tf.sparse.SparseTensor): + train_y_hot = tf.sparse.to_dense(train_y_hot) + + # Fit model + hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=50, + verbose=1, validation_data=(test_x, test_y)) +# hist = model.fit(train_dim, train_y_hot, batch_size=BATCH_SIZE, epochs=100, +# verbose=1, validation_data=(test_x, test_y), callbacks = [early_stop]) + + #print(hist.history.keys()) + + plt.figure(figsize=(12, 6)) + + # Loss plot + plt.subplot(1, 2, 1) + plt.plot(hist.history['loss'], label='Training Loss') + plt.plot(hist.history['val_loss'], label='Validation Loss') + plt.title('Loss over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Loss') + plt.legend() + + # Accuracy plot + plt.subplot(1, 2, 2) + plt.plot(hist.history['categorical_accuracy'], label='Training Accuracy') + plt.plot(hist.history['val_categorical_accuracy'], label='Validation Accuracy') + plt.title('Accuracy over Epochs') + plt.xlabel('Epochs') + plt.ylabel('Accuracy') + plt.legend() + + plt.tight_layout() + + # Ensure the plot is displayed + dirname2 = "/content/drive/MyDrive/DLP/DLP_autoXRD/plots" + + plot_filename = os.path.join(dirname2, f'training_validation_plots_{k}_dropout.png') + plt.savefig(plot_filename) + + #Compute model predictions + prediction=model.predict(test_x) + + #Go from one-hot to ordinal... + prediction_ord=[np.argmax(element) for element in prediction] + predictions_ord.append(prediction_ord) + + # Compute accuracy, recall, precision and F1 with macro and micro averaging + accuracy_exp[k] = metrics.accuracy_score(y_exp[test], prediction_ord) + accuracy_exp_r1[k] = metrics.recall_score(y_exp[test], prediction_ord, average='macro') + accuracy_exp_r2[k] = metrics.recall_score(y_exp[test], prediction_ord, average='micro') + accuracy_exp_p1[k] = metrics.precision_score(y_exp[test], prediction_ord, average='macro', zero_division=1) + accuracy_exp_p2[k] = metrics.precision_score(y_exp[test], prediction_ord, average='micro', zero_division=1) + f1[k]=metrics.f1_score(y_exp[test], prediction_ord, average='micro') + f1_m[k]=metrics.f1_score(y_exp[test], prediction_ord, average='macro') + + #Produce ground_truth, each list element contains array with test elements on first column with respect to X_exp and + ground_truth.append(np.concatenate([test.reshape(len(test),1),y_exp[test].reshape(len(test),1)],axis=1)) + + #Compute loss and accuracy for each k validation + accuracy.append(model.evaluate(test_x, test_y, verbose=0)) + + #Save logs + log = pd.DataFrame(hist.history) + logs.append(log) + + #Save models on current folder with names subscripts 0 to 4 + #model.save(os.path.join(dirname, 'keras_model')+str(k)+'.h5') + model.save(os.path.join(dirname, 'keras_model_dropout') + str(k) + '.keras') + +# +accuracy = np.array(accuracy) + +# Plot final cross validation accuracy +print ('Mean Cross-val accuracy', np.mean(accuracy[:,1])) + +################################################################ +# Plotting Class Activation Maps +################################################################ + +# Compute correctly classified and incorrectly classified cases +corrects, incorrects = find_incorrects(ground_truth,predictions_ord) + +#def get_cam(model_path, trains, X_exp): + # Load the model using keras.models.load_model +# model = keras.models.load_model(model_path, compile=False) + + # Compile the loaded model, ensure it's ready for use +# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + +# Get dataframe of all incorrects and dataframe of all corrects +corrects_df = pd.concat([r for r in corrects], ignore_index=False, axis=0) +incorrects_df = pd.concat([r for r in incorrects], ignore_index=False, axis=0) + +# Get the cam for the trained examples, for each class we average the cam of all trained examples +# Trains refers to the elements in X_exp used for training +# Choose the model in cross validation as output, in this case we plot number 5 + +cam_outputs=get_cam('keras_model4.keras', trains[4], X_exp) +#print(f"this is the cam_outputs",cam_outputs) +cam_df=pd.DataFrame(cam_outputs) +cam_df=cam_df.iloc[1:] +cam_df['Label']=y_exp[trains[4]] + +# CAM with all augmented training data +rng=range(0,7000) +cam_outputs2=get_cam('keras_model4.keras', rng, train_combine) +cam_df2=pd.DataFrame(cam_outputs2) +cam_df2=cam_df2.iloc[1:] +cam_df2['Label']=train_y + +# Now we focus on the incorrectly labelled cam's +incorrects_filtered=incorrects_df[incorrects_df.Model=='keras_model4.keras'] + +# Get CAM of incorrectly classified cases +cam_inc=get_cam('keras_model4.keras', [int(element) for element in incorrects_filtered.index], X_exp) +cam_inc=pd.DataFrame(cam_inc) +cam_inc=cam_inc.iloc[1:] + +# Redefine main variable +cam_df=cam_df2 + +#Get the average class maps for a certain class, in this case 6 + +cam_filtered=cam_df[cam_df.Label==6] +means_6=cam_filtered.mean() +means_6=means_6.iloc[:-1] + +#print(f"this is the input to the plot_cam",means_6) #check if the input is empty + +#Plot the average class map for class 6 +plot_cam(means_6,'Average CAM for Class 6 with dropout, trained model4.keras') + +#Plot correctly classified CAMs and patterns, no need to change this +corrects_filtered=corrects_df[corrects_df.Model=='keras_model4.keras'] +cam_cor=get_cam('keras_model4.keras', [int(element) for element in corrects_filtered.index], X_exp) +cam_cor=pd.DataFrame(cam_cor) +cam_cor=cam_cor.iloc[1:] + +#Plot a single correctly classified pattern +plot_cam(cam_cor.iloc[-1,:],'CAM for single pattern: true class 6 with dropout, predicted is 6', X_exp[int(corrects_filtered.index[-1])]) + +# Note that results may vary due to NN variability during training, make sure you are plotting +# correct and incorrect cases clearly + +################################################################ \ No newline at end of file diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000..03840a2 --- /dev/null +++ b/test.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMVolxnXuuwzlLI6bKQi1kc"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"wLxNTxyRhDDI"},"outputs":[],"source":[]}]} \ No newline at end of file