added the basic CNN training code

basicCNN.py

+### Ali Farahmand
+### CS 488 Senior Capstone
+
+
+#imports
+import glob
+import numpy as np
+import pandas as pd
+from keras.preprocessing.image import ImageDataGenerator
+from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from keras.models import Sequential
+from keras import optimizers
+from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+
+#commonly used variables
+batch_size = 10
+image_size = (96,96)
+epochs = 10
+
+
+#loading csv into dataframe
+labels = pd.read_csv("histopathologic-cancer-detection/train_labels.csv")
+
+
+#Creating the training and valiation sets
+labels_neg = labels[labels['label'] == 0].sample(80000, random_state=3)
+labels_pos = labels[labels['label'] == 1].sample(80000, random_state=3)
+
+val_neg = labels_neg[:8000]
+val_pos = labels_pos[:8000]
+
+labels_neg = pd.concat([labels_neg, val_neg]).drop_duplicates(keep=False)
+labels_pos = pd.concat([labels_pos, val_pos]).drop_duplicates(keep=False)
+
+train_labels = pd.concat([labels_neg, labels_pos])
+val_labels = pd.concat([val_neg, val_pos])
+
+#adding image format .tif to the end of each id
+def append_ext(fn):
+    return fn+".tif"
+train_labels["id"] = train_labels["id"].apply(append_ext)
+val_labels["id"] = val_labels["id"].apply(append_ext)
+
+
+#datagenerators and image augmentation
+train_datagen = ImageDataGenerator(rescale=1./255, zoom_range=0.3, rotation_range=50,
+									width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, 
+									horizontal_flip=True, fill_mode='nearest')
+
+val_datagen = ImageDataGenerator(rescale=1./255)
+
+
+train_generator = train_datagen.flow_from_dataframe(dataframe=train_labels, directory="histopathologic-cancer-detection/train", x_col="id", y_col="label", class_mode="raw", target_size=image_size, batch_size=batch_size)
+val_generator = val_datagen.flow_from_dataframe(dataframe=val_labels, directory="histopathologic-cancer-detection/train", x_col="id", y_col="label", class_mode="raw", target_size=image_size, batch_size=batch_size)
+
+#Creating the model
+model = Sequential()
+
+model.add(Conv2D(32, kernel_size=(3, 3), activation = 'relu', input_shape = (96, 96, 3)))
+model.add(Conv2D(32, kernel_size=(3, 3), activation = 'relu'))
+model.add(Conv2D(32, kernel_size=(3, 3), activation = 'relu'))
+model.add(MaxPooling2D(pool_size = (2,2))) 
+model.add(Dropout(0.3))
+
+model.add(Conv2D(64, kernel_size=(3, 3), activation ='relu'))
+model.add(Conv2D(64, kernel_size=(3, 3), activation ='relu'))
+model.add(Conv2D(64, kernel_size=(3, 3), activation ='relu'))
+model.add(MaxPooling2D(pool_size = (2,2)))
+model.add(Dropout(0.3))
+
+model.add(Conv2D(128, kernel_size=(3, 3), activation ='relu'))
+model.add(Conv2D(128, kernel_size=(3, 3), activation ='relu'))
+model.add(Conv2D(128, kernel_size=(3, 3), activation ='relu'))
+model.add(MaxPooling2D(pool_size = (2,2)))
+model.add(Dropout(0.3))
+
+model.add(Flatten())
+model.add(Dense(256, activation = "relu"))
+model.add(Dropout(0.3))
+model.add(Dense(1, activation = "sigmoid"))
+
+model.compile(loss='binary_crossentropy',
+			  optimizer=optimizers.Adam(lr=1e-4),
+              metrics=['accuracy'])
+
+model.summary()
+
+
+#saving the model at best val accuracy
+filepath = "model.h5"
+checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, 
+                             save_best_only=True, mode='max')
+#reducing the learning rate if val accuracy drops
+reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.5, patience=2, 
+                                   verbose=1, mode='max', min_lr=0.00001)
+                                                         
+callbacks_list = [checkpoint, reduce_lr]
+
+#training
+history = model.fit(train_generator, batch_size = batch_size, 
+                    validation_data=val_generator,
+                    validation_steps=50,
+                    epochs=epochs, verbose=1,
+                    callbacks = callbacks_list)
+
+#saving the accuracy and loss values
+f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
+t = f.suptitle('Basic CNN Performance', fontsize=12)
+f.subplots_adjust(top=0.85, wspace=0.3)
+
+epoch_list = list(range(1,epochs+1))
+ax1.plot(epoch_list, history.history['accuracy'], label='Train Accuracy')
+ax1.plot(epoch_list, history.history['val_accuracy'], label='Validation Accuracy')
+ax1.set_xticks(np.arange(0, epochs+1, 5))
+ax1.set_ylabel('Accuracy Value')
+ax1.set_xlabel('Epoch')
+ax1.set_title('Accuracy')
+l1 = ax1.legend(loc="best")
+
+ax2.plot(epoch_list, history.history['loss'], label='Train Loss')
+ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')
+ax2.set_xticks(np.arange(0, epochs+1, 5))
+ax2.set_ylabel('Loss Value')
+ax2.set_xlabel('Epoch')
+ax2.set_title('Loss')
+l2 = ax2.legend(loc="best")
+plt.savefig('plotmain.png')