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import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelBinarizer
import skluc.mldatasets as dataset
from fasfood_layer import fast_food
import matplotlib.pyplot as plt
IMAGE_SIZE = 24
enc = LabelBinarizer()
cifar = dataset.Cifar10Dataset()
cifar_d = cifar.load()
cifar_d = cifar.to_image()
X_train, Y_train = cifar_d["train"]
X_test, Y_test = cifar_d["test"]
X_train = np.array(X_train / 255)
enc.fit(Y_train)
Y_train = np.array(enc.transform(Y_train))
X_test = np.array(X_test / 255)
Y_test = np.array(enc.transform(Y_test))
X_train = X_train.astype(np.float32)
permut = np.random.permutation(X_train.shape[0])
val_size = 5000
X_val = X_train[permut[:val_size]]
X_train = X_train[permut[val_size:]]
Y_val = Y_train[permut[:val_size]]
Y_train = Y_train[permut[val_size:]]
X_test = X_test.astype(np.float32)
Y_train = Y_train.astype(np.float32)
Y_test = Y_test.astype(np.float32)
def distorded_inputs(image_tensor):
height = IMAGE_SIZE
width = IMAGE_SIZE
distorted_image = tf.random_crop(image_tensor, [height, width, 3])
distorted_image = tf.image.random_flip_left_right(distorted_image)
distorted_image = tf.image.random_brightness(distorted_image,
max_delta=63)
distorted_image = tf.image.random_contrast(distorted_image,
lower=0.2, upper=1.8)
float_image = tf.image.per_image_standardization(distorted_image)
return float_image
if __name__ == '__main__':
SIGMA = 5.0
print("Sigma = {}".format(SIGMA))
with tf.Graph().as_default():
output_dim = Y_train.shape[1]
input_dim = X_train.shape[1:]
x_image = tf.placeholder(tf.float32, shape=[None, *input_dim], name="x_image")
y_ = tf.placeholder(tf.float32, shape=[None, output_dim], name="labels")
tf.summary.image("cifarimage", x_image, max_outputs=10)
dist_x_images = distorded_inputs(x_image)
tf.summary.image("cifarimagedistorded", dist_x_images, max_outputs=10)
# out = fast_food(x_image, SIGMA)
merged_summary = tf.summary.merge_all()
init = tf.global_variables_initializer()
sess = tf.Session()
summary_writer = tf.summary.FileWriter("cifar")
feed_dict = {x_image: X_train[:10], y_: Y_train[:10]}
summary = sess.run([merged_summary], feed_dict=feed_dict)
summary_writer.add_summary(summary[0])
summary_writer.close()