preprocessing: best@64, 0.6857, truth: 0.9446/0.6690 -> 0.65896

hw3/test_cnn.py

@@ -14,7 +14,7 @@ def ensure_dir(file_path):
 height = width = 48
 num_classes = 7
 input_shape = (height, width, 1)
-model_name = 'try.h5'
+model_name = 'pre2.h5'
 
 # Read the test data
 with open(sys.argv[1], "r+") as f:

hw3/train_cnn.py

@@ -2,8 +2,9 @@
 import sys, os
 import numpy as np
 import keras
+from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
-from keras.layers import Dense, Activation, Flatten, Dropout
+from keras.layers import Dense, Activation, Flatten, Dropout, LeakyReLU
 from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
 from keras import losses
 from keras import optimizers
@@ -13,8 +14,9 @@
 num_classes = 7
 input_shape = (height, width, 1)
 batch_size = 128
-epochs = 20
-model_name = 'try.h5'
+epochs = 100
+zoom_range = 0.05
+model_name = 'pre2.h5'
 isValid = 1
 
 # Read the train data
@@ -32,42 +34,70 @@
 # Change data into CNN format
 X = X.reshape(X.shape[0], height, width, 1)
 
-X_train, Y_train = X, Y
+# 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:]]
+else:
+ X_train, Y_train = X, Y
 
 # Construct the model
 model = Sequential()
-model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
-model.add(Conv2D(64, (3, 3), activation='relu'))
-model.add(MaxPooling2D(pool_size=(3, 3)))
-model.add(Dropout(0.5))
-model.add(Conv2D(128, (3, 3), activation='relu'))
-model.add(Conv2D(256, (3, 3), activation='relu'))
+model.add(Conv2D(32, (3, 3), input_shape=input_shape))
+model.add(LeakyReLU(alpha=0.03))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(64, (3, 3)))
+model.add(LeakyReLU(alpha=0.03))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(128, (3, 3)))
+model.add(LeakyReLU(alpha=0.03))
 model.add(AveragePooling2D(pool_size=(2, 2)))
-model.add(Dropout(0.5))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(256, (3, 3)))
+model.add(LeakyReLU(alpha=0.03))
+model.add(AveragePooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
 model.add(Flatten())
+
 model.add(Dense(128, activation='relu'))
 model.add(Dropout(0.5))
 model.add(Dense(num_classes))
 model.add(Activation('softmax'))
+
 model.summary()
 
 # Compile the model
 model.compile(loss='categorical_crossentropy',
  optimizer='adam',
  metrics=['accuracy'])
 
+# Image PreProcessing
+train_gen = ImageDataGenerator(rotation_range=10,
+ width_shift_range=0.05,
+ height_shift_range=0.05,
+ shear_range=0.05,
+ zoom_range=[1-zoom_range, 1+zoom_range],
+ horizontal_flip=True)
+train_gen.fit(X_train)
+
 # Fit the model
 if isValid:
- model.fit(X_train, Y_train,
- batch_size=batch_size,
- epochs=epochs,
- verbose=1,
- validation_split=0.2)
+ 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,
+ validation_data=(X_valid, Y_valid))
 else:
- model.fit(X_train, Y_train,
- batch_size=batch_size,
- epochs=epochs,
- verbose=1)
+ 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)
 
 # Save model
 model.save(model_name)