mirror of
https://github.com/wassname/keras-contrib.git
synced 2026-08-17 11:20:47 +08:00
Somshubra Majumdar
bdc64bf2f3
* Began work on batch renormalization * Correct the implementation to use broadcasting properly, so it supports th dim ordering * Correct the implementation to use broadcasting properly, so it supports th dim ordering * Corrected normalizing term for d * Moved location of updates * Fixed the training problems and now auto adjusts the r_max and d_max values * Removed unused code comments * Finished implementation of batch renormalization * Corrected the get_config method * Added tests * Added user changeable parameter t_delta to change the rate at which time steps increase * Updated tests to state renorm everywhere * Attempt a fix for mode=1 test fail (giving nans) * Applied fix for nans to all calculations involving running mean and std * Corrected time delta default value * Reverts a mistake in pytest settings * Corrections to calculations of r and d due to wrong variable naming (running_std perserves running_variance) * Added t_delta to get_config * Fix commit change mixup * Corrected the check for uses_learning_flag * Made a few corrections, added moments to backend, added test to backend * Probable fix for test * Corrected backend test * Added support for mode 2
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
import tensorflow as tf
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from tensorflow.python.training import moving_averages
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from tensorflow.python.ops import tensor_array_ops
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from tensorflow.python.ops import control_flow_ops
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try:
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from tensorflow.python.ops import ctc_ops as ctc
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except ImportError:
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import tensorflow.contrib.ctc as ctc
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from keras import backend as K
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from keras.backend import tensorflow_backend as KTF
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import numpy as np
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import os
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import warnings
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from keras.backend.common import floatx, _EPSILON, image_dim_ordering, reset_uids
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from keras.backend.tensorflow_backend import _preprocess_conv3d_input
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from keras.backend.tensorflow_backend import _preprocess_conv3d_kernel
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from keras.backend.tensorflow_backend import _preprocess_border_mode
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from keras.backend.tensorflow_backend import _postprocess_conv3d_output
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from keras.backend.tensorflow_backend import _preprocess_border_mode
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py_all = all
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def _preprocess_deconv_output_shape(x, shape, dim_ordering):
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if dim_ordering == 'th':
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shape = (shape[0],) + tuple(shape[2:]) + (shape[1],)
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if shape[0] is None:
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shape = (tf.shape(x)[0], ) + tuple(shape[1:])
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shape = tf.stack(list(shape))
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return shape
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def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
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border_mode='valid',
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dim_ordering='default',
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image_shape=None, filter_shape=None):
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'''3D deconvolution (i.e. transposed convolution).
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# Arguments
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x: input tensor.
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kernel: kernel tensor.
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output_shape: 1D int tensor for the output shape.
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strides: strides tuple.
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border_mode: string, "same" or "valid".
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dim_ordering: "tf" or "th".
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Whether to use Theano or TensorFlow dimension ordering
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for inputs/kernels/ouputs.
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# Returns
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A tensor, result of transposed 3D convolution.
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# Raises
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ValueError: if `dim_ordering` is neither `tf` or `th`.
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'''
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if dim_ordering == 'default':
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dim_ordering = image_dim_ordering()
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if dim_ordering not in {'th', 'tf'}:
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raise ValueError('Unknown dim_ordering ' + str(dim_ordering))
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x = _preprocess_conv3d_input(x, dim_ordering)
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output_shape = _preprocess_deconv_output_shape(x, output_shape, dim_ordering)
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kernel = _preprocess_conv3d_kernel(kernel, dim_ordering)
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kernel = tf.transpose(kernel, (0, 1, 2, 4, 3))
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padding = _preprocess_border_mode(border_mode)
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strides = (1,) + strides + (1,)
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x = tf.nn.conv3d_transpose(x, kernel, output_shape, strides,
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padding=padding)
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return _postprocess_conv3d_output(x, dim_ordering)
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def extract_image_patches(X, ksizes, ssizes, border_mode="same", dim_ordering="tf"):
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'''
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Extract the patches from an image
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Parameters
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----------
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X : The input image
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ksizes : 2-d tuple with the kernel size
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ssizes : 2-d tuple with the strides size
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border_mode : 'same' or 'valid'
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dim_ordering : 'tf' or 'th'
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Returns
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-------
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The (k_w,k_h) patches extracted
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TF ==> (batch_size,w,h,k_w,k_h,c)
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TH ==> (batch_size,w,h,c,k_w,k_h)
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'''
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kernel = [1, ksizes[0], ksizes[1], 1]
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strides = [1, ssizes[0], ssizes[1], 1]
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padding = _preprocess_border_mode(border_mode)
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if dim_ordering == "th":
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X = KTF.permute_dimensions(X, (0, 2, 3, 1))
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bs_i, w_i, h_i, ch_i = KTF.int_shape(X)
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patches = tf.extract_image_patches(X, kernel, strides, [1, 1, 1, 1], padding)
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# Reshaping to fit Theano
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bs, w, h, ch = KTF.int_shape(patches)
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patches = tf.reshape(tf.transpose(tf.reshape(patches, [bs, w, h, -1, ch_i]), [0, 1, 2, 4, 3]),
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[bs, w, h, ch_i, ksizes[0], ksizes[1]])
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if dim_ordering == "tf":
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patches = KTF.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])
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return patches
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def moments(x, axes, shift=None, keep_dims=False):
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''' Wrapper over tensorflow backend call '''
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return tf.nn.moments(x, axes, shift=shift, keep_dims=keep_dims)
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