From b30e8f3f1e150d7782cae5706f4bc746e04bb30b Mon Sep 17 00:00:00 2001
From: Luc Giffon <luc.giffon@lif.univ-mrs.fr>
Date: Mon, 22 Jan 2018 09:22:11 +0100
Subject: [PATCH] ajout de la version avec cifar10 [WIP]
---
main/deepfriedConvnetCifar10.py | 77 +++++++++++++++++++++++++++++++++
1 file changed, 77 insertions(+)
create mode 100644 main/deepfriedConvnetCifar10.py
diff --git a/main/deepfriedConvnetCifar10.py b/main/deepfriedConvnetCifar10.py
new file mode 100644
index 0000000..747b4c1
--- /dev/null
+++ b/main/deepfriedConvnetCifar10.py
@@ -0,0 +1,77 @@
+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()
+
--
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