add checkpoint & csv log

hw3/train_cnn.py

@@ -1,23 +1,24 @@
 #!/usr/bin/env python
 import sys, os
 import numpy as np
-import keras
+from keras.utils import to_categorical
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
 from keras.layers import Dense, Activation, Flatten, Dropout, LeakyReLU
 from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
 from keras.layers import BatchNormalization
 from keras import losses
 from keras import optimizers
+from keras.callbacks import CSVLogger, ModelCheckpoint
 
 # Parameter
 height = width = 48
 num_classes = 7
 input_shape = (height, width, 1)
 batch_size = 128
-epochs = 100
-zoom_range = 0.05
-model_name = 'pre5.h5'
+epochs = 300
+zoom_range = 0.2
+model_name = 'pre6.h5'
 isValid = 1
 
 # Read the train data
@@ -30,33 +31,34 @@
  Y = data[:, 0]
  X /= 255
  Y = Y.reshape(Y.shape[0], 1)
- Y = keras.utils.to_categorical(Y, num_classes)
+ Y = to_categorical(Y, num_classes)
 
 # Change data into CNN format
 X = X.reshape(X.shape[0], height, width, 1)
 
 # Split the data
 if isValid:
  valid_num = 3000
- permu = np.random.permutation(X.shape[0])
- X_train, Y_train = X[permu[:-valid_num]], Y[permu[:-valid_num]]
- X_valid, Y_valid = X[permu[-valid_num:]], Y[permu[-valid_num:]]
+ X_train, Y_train = X[:-valid_num], Y[:-valid_num]
+ X_valid, Y_valid = X[-valid_num:], Y[-valid_num:]
+
 else:
  X_train, Y_train = X, Y
 
 # Construct the model
 model = Sequential()
+
 model.add(Conv2D(64, (3, 3), padding='same', input_shape=input_shape))
 model.add(LeakyReLU(alpha=0.03))
 model.add(BatchNormalization())
 model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
-model.add(Dropout(0.25))
+model.add(Dropout(0.2))
 
 model.add(Conv2D(128, (3, 3), padding='same'))
 model.add(LeakyReLU(alpha=0.03))
 model.add(BatchNormalization())
 model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
-model.add(Dropout(0.3))
+model.add(Dropout(0.25))
 
 model.add(Conv2D(256, (3, 3), padding='same'))
 model.add(LeakyReLU(alpha=0.03))
@@ -72,7 +74,7 @@
 
 model.add(Flatten())
 
-model.add(Dense(128, activation='relu'))
+model.add(Dense(256, activation='relu'))
 model.add(BatchNormalization())
 model.add(Dropout(0.5))
 model.add(Dense(512, activation='relu'))
@@ -89,24 +91,33 @@
  metrics=['accuracy'])
 
 # Image PreProcessing
-train_gen = ImageDataGenerator(rotation_range=10,
- width_shift_range=0.05,
- height_shift_range=0.05,
- shear_range=0.05,
+train_gen = ImageDataGenerator(rotation_range=25,
+ width_shift_range=0.1,
+ height_shift_range=0.1,
+ shear_range=0.1,
  zoom_range=[1-zoom_range, 1+zoom_range],
  horizontal_flip=True)
 train_gen.fit(X_train)
 
+# Callbacks
+callbacks = []
+modelcheckpoint = ModelCheckpoint('ckpt/weights.{epoch:03d}-{val_acc:.5f}.h5', monitor='val_acc', save_best_only=True)
+callbacks.append(modelcheckpoint)
+csv_logger = CSVLogger('cnn_log.csv', separator=',', append=False)
+callbacks.append(csv_logger)
+
 # Fit the model
 if isValid:
  model.fit_generator(train_gen.flow(X_train, Y_train, batch_size=batch_size),
  steps_per_epoch=10*X_train.shape[0]//batch_size,
  epochs=epochs,
+ callbacks=callbacks,
  validation_data=(X_valid, Y_valid))
 else:
  model.fit_generator(train_gen.flow(X_train, Y_train, batch_size=batch_size),
  steps_per_epoch=10*X_train.shape[0]//batch_size,
- epochs=epochs)
+ epochs=epochs,
+ callbacks=callbacks)
 
 # Save model
 model.save(model_name)