training fold

urbanclass_fold.py

+import numpy as np 
+
+import pandas as pd 
+
+
+
+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
+
+
+
+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 train_test_split(fold_k, data, X_dim=(128, 128, 1)):
+    X_train = np.stack(data[data.fold != fold_k].melspectrogram.to_numpy())
+    X_test = np.stack(data[data.fold == fold_k].melspectrogram.to_numpy())
+
+    y_train = data[data.fold != fold_k].label.to_numpy()
+    y_test = data[data.fold == fold_k].label.to_numpy()
+
+    XX_train = X_train.reshape(X_train.shape[0], *X_dim)
+    XX_test = X_test.reshape(X_test.shape[0], *X_dim)
+    
+    yy_train = to_categorical(y_train)
+    yy_test = to_categorical(y_test)
+    
+    return XX_train, XX_test, yy_train, yy_test
+
+
+
+def show_results(tot_history):
+    """Show accuracy and loss graphs for train and test sets."""
+    pngName = ['result/res1.png','result/res2.png','result/res3.png','result/res4.png','result/res5.png','result/res6.png','result/res7.png',
+               'result/res8.png','result/res9.png','result/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('result/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']))
+
+
+def process_fold(fold_k, data, epochs=100, num_batch_size=32):
+    # split the data
+    X_train, X_test, y_train, y_test = train_test_split(fold_k, data)
+
+    # init model
+    model = init_model()
+
+    # pre-training accuracy
+    log_dir = "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
+
+    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir = log_dir, histogram_freq = 1)
+
+    
+    # train the model
+
+    history = model.fit(X_train,y_train, epochs=epochs,validation_data=(X_test,y_test),batch_size=num_batch_size, callbacks=[tensorboard_callback])
+    
+    return history
+
+
+if __name__ == "__main__":
+    
+    history1 = []
+    for i in range(10) : 
+        FOLD = i+1
+        
+        history = process_fold(FOLD_K, us8k_df, epochs=3)
+        history1.append(history)
+
+
+    show_results(history1)