urban classif

urban_classif_augmented_fold.py

+import numpy as np 
+import os
+import pandas as pd 
+from scipy.io import wavfile
+
+import librosa
+from tqdm import tqdm
+import tensorflow as tf
+from tensorflow import keras
+
+import matplotlib.pyplot as plt
+from tensorflow.keras import regularizers, activations
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D, GlobalAveragePooling2D
+from tensorflow.keras.utils import to_categorical
+
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+from datetime import datetime 
+
+from matplotlib import pyplot as plt
+
+from sklearn.model_selection import train_test_split
+
+
+
+def loading_data(file_train,file_test) : 
+
+    us8k_df_train = pd.read_pickle(file_train)
+    us8k_df_test = pd.read_pickle(file_test)
+
+
+    df_train = us8k_df_train.drop(['fold'],axis=1)
+    df_test = us8k_df_test.drop(['fold'],axis=1)
+
+
+
+    X_train = np.stack(df_train.melspectrogram.to_numpy())
+    X_test = np.stack(df_test.melspectrogram.to_numpy())
+
+
+
+    X_dim = (128,128,1)
+    X_train = X_train.reshape(X_train.shape[0], *X_dim)
+    X_test = X_test.reshape(X_test.shape[0], *X_dim)
+
+
+    Y_train = np.array(df_train['label'])
+    Y_train = to_categorical(Y_train)
+        
+    Y_test = np.array(df_test['label'])
+    Y_test = to_categorical(Y_test)
+
+
+
+    return X_train,Y_train,X_test,Y_test
+
+
+def init_model():
+    model1 = Sequential()
+    
+    #layer-1
+    model1.add(Conv2D(filters=24, kernel_size=5, input_shape=(128, 128, 1),
+                      kernel_regularizer=regularizers.l2(1e-3)))
+    model1.add(MaxPooling2D(pool_size=(3,3), strides=3))
+    model1.add(Activation(activations.relu))
+    
+    #layer-2
+    model1.add(Conv2D(filters=36, kernel_size=4, padding='valid', kernel_regularizer=regularizers.l2(1e-3)))
+    model1.add(MaxPooling2D(pool_size=(2,2), strides=2))
+    model1.add(Activation(activations.relu))
+    
+    #layer-3
+    model1.add(Conv2D(filters=48, kernel_size=3, padding='valid'))
+    model1.add(Activation(activations.relu))
+    
+    model1.add(GlobalAveragePooling2D())
+    
+    #layer-4 (1st dense layer)
+    model1.add(Dense(60, activation='relu'))
+    model1.add(Dropout(0.5))
+    
+    #layer-5 (2nd dense layer)
+    model1.add(Dense(10, activation='softmax'))
+
+    
+    # compile
+    model1.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
+    
+    return model1   
+
+
+def show_results(tot_history):
+    """Show accuracy and loss graphs for train and test sets."""
+    pngName = ['resultaugmentedfold/res1.png','resultaugmentedfold/res2.png','resultaugmentedfold/res3.png','resultaugmentedfold/res4.png','resultaugmentedfold/res5.png','resultaugmentedfold/res6.png','resultaugmentedfold/res7.png','resultaugmentedfold/res8.png','resultaugmentedfold/res9.png','resultaugmentedfold/res10.png']
+    for i, history in enumerate(tot_history):
+        print('\n({})'.format(i+1))
+        fig = plt.figure()
+        plt.figure(figsize=(15,5))
+
+        plt.subplot(121)
+        plt.plot(history.history['accuracy'])
+        plt.plot(history.history['val_accuracy'])
+        plt.grid(linestyle='--')
+        plt.ylabel('Accuracy')
+        plt.xlabel('Epoch')
+        plt.legend(['train', 'validation'], loc='upper left')
+
+        plt.subplot(122)
+        plt.plot(history.history['loss'])
+        plt.plot(history.history['val_loss'])
+        plt.grid(linestyle='--')
+        plt.ylabel('Loss')
+        plt.xlabel('Epoch')
+        plt.legend(['train', 'validation'], loc='upper left')
+            
+        plt.savefig(pngName[i])
+        
+        file = open('resultaugmentedfold/result.txt','a')
+        file.write('\n=========FOLD%s=========\n'% (i+1))
+        file.write('\nMax validation accuracy: %.4f %%' % (np.max(history.history['val_accuracy']) * 100))
+        file.write('\nMin validation loss: %.5f' % np.min(history.history['val_loss']))
+
+        print('\tMax validation accuracy: %.4f %%' % (np.max(history.history['val_accuracy']) * 100))
+        print('\tMin validation loss: %.5f' % np.min(history.history['val_loss']))
+
+
+
+
+
+
+if __name__ == "__main__":
+    history = []
+    test_list = [1,2,3,4,5,6,7,8,9]
+    for test in test_list:
+        filename_train = 'fold_pkl/train_augmented'+str(test)+'.pkl'
+        filename_test = 'fold_pkl/test'+str(test)+'.pkl'
+        print(filename_test)
+        X_train,Y_train,X_test,Y_test = loading_data(filename_train,filename_test)
+
+        model = init_model()
+        log_dir = "logs/fit/afold" + datetime.now().strftime("%Y%m%d-%H%M%S")
+
+        tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir = log_dir, histogram_freq = 1)
+        save_best = tf.keras.callbacks.ModelCheckpoint(filepath = "logs/checkpoints/", save_weights_only = True,
+                                                    monitor = "val_accuracy", mode = "max", save_best_only = True)
+
+        initial_epochs = 100
+        num_batch_size = 32
+        model_fit = model.fit(X_train,Y_train, epochs=initial_epochs,validation_data=(X_test,Y_test),
+                                batch_size=num_batch_size, callbacks = [tensorboard_callback, save_best])
+
+
+        model.save('logs/soundClass_augmented_model'+str(test)+'.h5')   
+        
+        history.append(model_fit)
+    show_results(history)
+        
+    
+
+