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‎04 VGG Tensorflow/VGG.py‎

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import tensorflow as tf
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import tools
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#%%
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def VGG16(x, n_classes, is_pretrain=True):
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x = tools.conv('conv1_1', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv1_2', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.pool('pool1', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv2_1', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv2_2', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.pool('pool2', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv3_1', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv3_2', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv3_3', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv4_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv4_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv4_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv5_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv5_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv5_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.FC_layer('fc6', x, out_nodes=4096)
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x = tools.batch_norm(x)
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x = tools.FC_layer('fc7', x, out_nodes=4096)
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x = tools.batch_norm(x)
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x = tools.FC_layer('fc8', x, out_nodes=n_classes)
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return x
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#%% TO get better tensorboard figures!
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def VGG16N(x, n_classes, is_pretrain=True):
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with tf.name_scope('VGG16'):
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x = tools.conv('conv1_1', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv1_2', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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with tf.name_scope('pool1'):
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x = tools.pool('pool1', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv2_1', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv2_2', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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with tf.name_scope('pool2'):
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x = tools.pool('pool2', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv3_1', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv3_2', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv3_3', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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with tf.name_scope('pool3'):
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x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv4_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv4_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv4_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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with tf.name_scope('pool4'):
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x = tools.pool('pool4', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.conv('conv5_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv5_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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x = tools.conv('conv5_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
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with tf.name_scope('pool5'):
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x = tools.pool('pool5', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
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x = tools.FC_layer('fc6', x, out_nodes=4096)
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with tf.name_scope('batch_norm1'):
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x = tools.batch_norm(x)
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x = tools.FC_layer('fc7', x, out_nodes=4096)
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with tf.name_scope('batch_norm2'):
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x = tools.batch_norm(x)
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x = tools.FC_layer('fc8', x, out_nodes=n_classes)
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return x
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#%%
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‎04 VGG Tensorflow/input_data.py‎

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import tensorflow as tf
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import numpy as np
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import os
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#%% Reading data
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def read_cifar10(data_dir, is_train, batch_size, shuffle):
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"""Read CIFAR10
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Args:
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data_dir: the directory of CIFAR10
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is_train: boolen
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batch_size:
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shuffle:
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Returns:
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label: 1D tensor, tf.int32
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image: 4D tensor, [batch_size, height, width, 3], tf.float32
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"""
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img_width = 32
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img_height = 32
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img_depth = 3
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label_bytes = 1
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image_bytes = img_width*img_height*img_depth
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with tf.name_scope('input'):
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if is_train:
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filenames = [os.path.join(data_dir, 'data_batch_%d.bin' %ii)
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for ii in np.arange(1, 6)]
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else:
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filenames = [os.path.join(data_dir, 'test_batch.bin')]
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filename_queue = tf.train.string_input_producer(filenames)
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reader = tf.FixedLengthRecordReader(label_bytes + image_bytes)
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key, value = reader.read(filename_queue)
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record_bytes = tf.decode_raw(value, tf.uint8)
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label = tf.slice(record_bytes, [0], [label_bytes])
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label = tf.cast(label, tf.int32)
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image_raw = tf.slice(record_bytes, [label_bytes], [image_bytes])
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image_raw = tf.reshape(image_raw, [img_depth, img_height, img_width])
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image = tf.transpose(image_raw, (1,2,0)) # convert from D/H/W to H/W/D
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image = tf.cast(image, tf.float32)
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# # data argumentation
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# image = tf.random_crop(image, [24, 24, 3])# randomly crop the image size to 24 x 24
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# image = tf.image.random_flip_left_right(image)
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# image = tf.image.random_brightness(image, max_delta=63)
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# image = tf.image.random_contrast(image,lower=0.2,upper=1.8)
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image = tf.image.per_image_standardization(image) #substract off the mean and divide by the variance
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if shuffle:
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images, label_batch = tf.train.shuffle_batch(
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[image, label],
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batch_size = batch_size,
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num_threads= 64,
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capacity = 20000,
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min_after_dequeue = 3000)
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else:
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images, label_batch = tf.train.batch(
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[image, label],
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batch_size = batch_size,
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num_threads = 64,
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capacity= 2000)
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## ONE-HOT
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n_classes = 10
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label_batch = tf.one_hot(label_batch, depth= n_classes)
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label_batch = tf.cast(label_batch, dtype=tf.int32)
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label_batch = tf.reshape(label_batch, [batch_size, n_classes])
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return images, label_batch
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#%%
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‎04 VGG Tensorflow/tool_show_feature_map.py‎

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import tensorflow as tf
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import numpy as np
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import matplotlib.pyplot as plt
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from PIL import Image
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import tools
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#%%
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def show_feature_map():
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cat = plt.imread('cat.jpg') #unit8
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plt.imshow(cat)
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cat = tf.cast(cat, tf.float32) #[360, 300, 3]
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x = tf.reshape(cat, [1, 360, 300,3]) #[1, 360, 300, 3]
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out = 25
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with tf.variable_scope('conv1'):
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w = tools.weight([3,3,3,out], is_uniform=True)
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x_w = tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
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b = tools.bias([out])
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x_b = tf.nn.bias_add(x_w, b)
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x_relu = tf.nn.relu(x_b)
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n_feature = int(x_w.get_shape()[-1])
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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feature_map = tf.reshape(x_w, [360,300,out])
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images = tf.image.convert_image_dtype (feature_map, dtype=tf.uint8)
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images = sess.run(images)
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plt.figure(figsize=(10, 10))
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for i in np.arange(0, n_feature):
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plt.subplot(5, 5, i + 1)
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plt.axis('off')
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plt.imshow(images[:,:,i])
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plt.show()
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show_feature_map()
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#%%
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def show_rich_feature():
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cat = plt.imread('cat.jpg') #unit8
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plt.imshow(cat)
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cat = tf.cast(cat, tf.float32) #[360, 300, 3]
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x = tf.reshape(cat, [1, 360, 300,3]) #[1, 360, 300, 3]
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with tf.variable_scope('conv1_1', reuse=True):
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w1 = tf.get_variable('weights', (3,3,3,64))
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b1 = tf.get_variable('biases', (64))
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x_w = tf.nn.conv2d(x, w1, strides=[1, 1, 1, 1], padding='SAME')
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x_b = tf.nn.bias_add(x_w, b1)
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x_relu = tf.nn.relu(x_b)
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out = 64
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n_feature = int(x_w.get_shape()[-1])
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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feature_map = tf.reshape(x_relu, [360,300,out])
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images = tf.image.convert_image_dtype (feature_map, dtype=tf.uint8)
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images = sess.run(images)
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plt.figure(figsize=(10, 10))
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for i in np.arange(0, 25):
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plt.subplot(5, 5, i + 1)
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plt.axis('off')
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plt.imshow(images[:,:,i])
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plt.show()
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#%%
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‎04 VGG Tensorflow/tool_show_size.py‎

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import tensorflow as tf
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import matplotlib.pyplot as plt
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import tools
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#%%
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cat = plt.imread('cat.jpg') #unit8
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plt.imshow(cat)
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cat = tf.cast(cat, tf.float32) #[360, 300, 3]
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x = tf.reshape(cat, [1, 360, 300, 3]) #[1, 360, 300, 3]
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#%%
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# First conv
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with tf.variable_scope('conv1'):
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w = tools.weight([3,3,3,16], is_uniform=True)
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x_w = tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
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b = tools.bias([16])
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x_b = tf.nn.bias_add(x_w, b)
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x_relu = tf.nn.relu(x_b)
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x_pool = tools.pool('test1', x_relu, kernel=[1,2,2,1], stride=[1,2,2,1],is_max_pool=True)
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# Second conv
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with tf.variable_scope('conv2'):
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w2 = tools.weight([3,3,16,32], is_uniform=True)
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x_w2 = tf.nn.conv2d(x_pool, w2, strides=[1, 1, 1, 1], padding='SAME')
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b2 = tools.bias([32])
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x_b2 = tf.nn.bias_add(x_w2, b2)
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x_relu2 = tf.nn.relu(x_b2)
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x_pool2 = tools.pool('test2',x_relu2, kernel=[1,2,2,1],stride=[1,2,2,1], is_max_pool=False)
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x_BN = tools.batch_norm(x_pool2)
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#%%
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def shape(x):
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return str(x.get_shape())
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## First conv
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print('\n')
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print('** First conv: **\n')
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print('input size: ', shape(x))
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print('w size:', shape(w))
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print('x_w size: ', shape(x_w))
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print('b size: ', shape(b))
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print('x_b size: ', shape(x_b))
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print('x_relu size: ', shape(x_relu))
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print('x_pool size: ', shape(x_pool))
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print('\n')
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## Second conv
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print('** Second conv: **\n')
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print('input size: ', shape(x_pool))
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print('w2 size:', shape(w2))
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print('x_w2 size: ', shape(x_w2))
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print('b2 size: ', shape(b2))
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print('x_b2 size: ', shape(x_b2))
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print('x_relu2 size: ', shape(x_relu2))
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print('x_pool2 size: ', shape(x_pool2))
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print('x_BN size: ', shape(x_BN))
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print('\n')
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‎04 VGG Tensorflow/tool_variable_name.py‎

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import tensorflow as tf
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#%%
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# First conv
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with tf.variable_scope('conv1'):
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w = tf.get_variable(name='weights',
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shape=[3,3,3,16],
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initializer=tf.contrib.layers.xavier_initializer(), trainable=False)
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b = tf.get_variable(name='biases',
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shape=[16],
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initializer=tf.constant_initializer(0.0), trainable=False)
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print('w name: ', w.name)
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print('b name:', b.name)
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with tf.name_scope('conv1'):
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w = tf.get_variable(name='weights',
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shape=[3,3,3,16],
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initializer=tf.contrib.layers.xavier_initializer())
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b = tf.get_variable(name='biases',
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shape=[16],
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initializer=tf.constant_initializer(0.0))
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print('w name: ', w.name)
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print('b name:', b.name)
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