Rename CNN.py to CNN-trainer.py

CNN-trainer.py

@@ -0,0 +1,112 @@
+from __future__ import print_function
+import os
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.callbacks import ModelCheckpoint
+from aura.aura_loader import get_data
+import matplotlib.pyplot as plt
+from time import time
+
+print("Modules imported.")
+print("Current Working Directory" + os.getcwd())
+
+# Prepare paths for GCP training
+root = "../Aura_Data/"
+train_paths = [root + "{136x136x199063}HealthyTrainset.aura", root + "{256x256x63198}RIDERTrainset.aura",
+               root + "{256x256x7918}BTPTrainset.aura"]
+test_paths = [root + "{136x136x22118}HealthyTestset.aura", root + "{256x256x7021}RIDERTestset.aura",
+              root + "{256x256x879}BTPTestset.aura"]
+
+# Prepare paths for local training experimentation
+# root = "../Aura_Data/Chunked/Dataset/"
+# train_paths = [root + "{136x136x181}HealthyTrainset.aura", root + "{256x256x270}CancerTrainset.aura"]
+# test_paths = [root + "{136x136x181}HealthyTestset.aura", root + "{256x256x270}CancerTestset.aura"]
+
+train_data, train_label = get_data(train_paths)
+test_data, test_label = get_data(test_paths)
+
+train_n, train_l, train_w = train_data.shape
+test_n, test_l, test_w = test_data.shape
+
+# Set up CNN
+batch_size = 32
+num_classes = 3
+epochs = 10
+# input image dimensions
+img_rows, img_cols = train_l, train_w
+
+y_train = train_label.copy()
+y_test = test_label.copy()
+
+x_train = train_data.reshape(train_n, train_l, train_w, 1)
+x_test = test_data.reshape(test_n, test_l, test_w, 1)
+
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+print(str(num_classes) + " classes set.")
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+model = Sequential()
+
+# Convolutional layers and Max pooling
+model.add(Conv2D(32, kernel_size=(16, 16),
+                 activation='relu',
+                 input_shape=(train_l, train_w, 1)))
+model.add(MaxPooling2D(pool_size=(8, 8)))
+model.add(Conv2D(64, (4, 4), activation='relu'))
+model.add(MaxPooling2D(pool_size=(4, 4)))
+model.add(Conv2D(128, (2, 2), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(256, (2, 2), activation='relu'))
+
+# Dense layers and output
+model.add(Flatten())
+model.add(Dense(1024, activation='relu'))
+model.add(Dropout(0.01))
+model.add(Dense(2048, activation='relu'))
+model.add(Dense(1024, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(512, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(256, activation='relu'))
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.1))
+model.add(Dense(64, activation='relu'))
+model.add(Dense(32, activation='relu'))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adadelta(),
+              metrics=['accuracy'])
+
+# checkpoint
+filepath = "weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
+checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
+callbacks_list = [checkpoint]
+
+history = model.fit(x_train, y_train,
+                    callbacks=callbacks_list,
+                    batch_size=batch_size,
+                    epochs=epochs,
+                    verbose=1,
+                    validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+finish_time = str(time.time())
+model.save("model" + finish_time[:finish_time.find(".")] + ".hf")
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])
+
+plt.plot(history.history['loss'])
+print(history.history['loss'])
+print(history.history['val_loss'])
+plt.plot(history.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()