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features.py
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features.py
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import tensorflow as tf
from utilities import conv2d_pool_block, conv2d_transpose_layer, dense_layer, dense_block
def extract_features_omniglot(images, output_size, use_batch_norm, dropout_keep_prob):
"""
Based on the architecture described in 'Matching Networks for One-Shot Learning'
http://arxiv.org/abs/1606.04080.pdf.
:param images: batch of images.
:param output_size: dimensionality of the output features.
:param use_batch_norm: whether to use batch normalization or not.
:param dropout_keep_prob: keep probability parameter for dropout.
:return: features.
"""
# 4X conv2d + pool blocks
h = conv2d_pool_block(images, use_batch_norm, dropout_keep_prob, 'same', 'fe_block_1')
h = conv2d_pool_block(h, use_batch_norm, dropout_keep_prob, 'same', 'fe_block_2')
h = conv2d_pool_block(h, use_batch_norm, dropout_keep_prob, 'same', 'fe_block_3')
h = conv2d_pool_block(h, use_batch_norm, dropout_keep_prob, 'same', 'fe_block_4')
# flatten output
h = tf.contrib.layers.flatten(h)
return h
def extract_features_mini_imagenet(images, output_size, use_batch_norm, dropout_keep_prob):
"""
Based on the architecture described in 'Matching Networks for One-Shot Learning'
http://arxiv.org/abs/1606.04080.pdf.
:param images: batch of images.
:param output_size: dimensionality of the output features.
:param use_batch_norm: whether to use batch normalization or not.
:param dropout_keep_prob: keep probability parameter for dropout.
:return: features.
"""
# 5X conv2d + pool blocks
h = conv2d_pool_block(images, use_batch_norm, dropout_keep_prob, 'valid', 'fe_block_1')
h = conv2d_pool_block(h, use_batch_norm, dropout_keep_prob, 'valid', 'fe_block_2')
h = conv2d_pool_block(h, use_batch_norm, dropout_keep_prob, 'valid', 'fe_block_3')
h = conv2d_pool_block(h, use_batch_norm, dropout_keep_prob, 'valid', 'fe_block_4')
h = conv2d_pool_block(h, use_batch_norm, dropout_keep_prob, 'valid', 'fe_block_5')
# flatten output
h = tf.contrib.layers.flatten(h)
return h