cifar-10_test_dropout.py

import keras
from keras.datasets import cifar10
import os
import math
import numpy as np
from keras.layers import Conv2D, Input, BatchNormalization, LeakyReLU
from keras.layers import Dense, Dropout, Flatten
from keras import Model
import WaveletLayer as wl

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def build_haar_matrix(row_size):
    size = (int)(row_size / 2)
    haarM1 = np.zeros((size, row_size))
    for i in range(size):
        haarM1[i][2 * i] = math.sqrt(2) / 2
        haarM1[i][2 * i + 1] = math.sqrt(2) / 2

    haarM2 = np.zeros((size, row_size))
    for i in range(size):
        haarM2[i][2 * i] = math.sqrt(2) / 2
        haarM2[i][2 * i + 1] = -math.sqrt(2) / 2

    m = np.concatenate((haarM1, haarM2), axis=0)
    return m

haarMatrix32 = build_haar_matrix(32)
haarMatrix16 = build_haar_matrix(16)
haarMatrix8 = build_haar_matrix(8)

num_classes = 10
epochs = 100
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'

# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape, 'train samples')
print(x_test.shape[0], 'test samples')
print(y_train.shape)
print(type(y_train[0]))

# 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)

input = Input(shape=(32, 32, 3))

conv1 = Conv2D(32, kernel_size=(5,5), padding='same')(input)
batch1 = BatchNormalization()(conv1)
relu1 = LeakyReLU(alpha=0)(batch1)
pool1 = wl.MyLayer(output_dim=(None, 16, 16, 32), haar_matrix=haarMatrix32)(relu1)

conv2 = Conv2D(32, kernel_size=(5,5), padding='same')(pool1)
batch2 = BatchNormalization()(conv2)
relu2 = LeakyReLU(alpha=0)(batch2)
pool2 = wl.MyLayer(output_dim=(None, 8, 8, 32), haar_matrix=haarMatrix16)(relu2)


conv3 = Conv2D(32, kernel_size=(5,5), padding='same')(pool2)
batch3 = BatchNormalization()(conv3)
relu3 = LeakyReLU(alpha=0)(batch3)
pool3 = wl.MyLayer(output_dim=(None, 4, 4, 32), haar_matrix=haarMatrix8)(relu3)

drop = Dropout(rate=0.1)(pool3)
conv4 = Conv2D(64, kernel_size=(4,4), padding='same')(drop)
relu3 = LeakyReLU(alpha=0)(conv4)
drop2 = Dropout(rate=0.1)(relu3)
conv5 = Conv2D(10, kernel_size=(1,1), padding='same')(drop2)
flat = Flatten()(conv5)
activ = Dense(units=10, activation='softmax')(flat)
model = Model(inputs=input, outputs=activ)

model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])


model.fit(x_train, y_train, epochs=2, batch_size=32)


loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)



print(loss_and_metrics)