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| # Copyright 2017 The TensorFlow Authors All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Functions to support building models for StreetView text transcription.""" | |
| import tensorflow as tf | |
| from tensorflow.contrib import slim | |
| def logits_to_log_prob(logits): | |
| """Computes log probabilities using numerically stable trick. | |
| This uses two numerical stability tricks: | |
| 1) softmax(x) = softmax(x - c) where c is a constant applied to all | |
| arguments. If we set c = max(x) then the softmax is more numerically | |
| stable. | |
| 2) log softmax(x) is not numerically stable, but we can stabilize it | |
| by using the identity log softmax(x) = x - log sum exp(x) | |
| Args: | |
| logits: Tensor of arbitrary shape whose last dimension contains logits. | |
| Returns: | |
| A tensor of the same shape as the input, but with corresponding log | |
| probabilities. | |
| """ | |
| with tf.variable_scope('log_probabilities'): | |
| reduction_indices = len(logits.shape.as_list()) - 1 | |
| max_logits = tf.reduce_max( | |
| logits, reduction_indices=reduction_indices, keep_dims=True) | |
| safe_logits = tf.subtract(logits, max_logits) | |
| sum_exp = tf.reduce_sum( | |
| tf.exp(safe_logits), | |
| reduction_indices=reduction_indices, | |
| keep_dims=True) | |
| log_probs = tf.subtract(safe_logits, tf.log(sum_exp)) | |
| return log_probs | |
| def variables_to_restore(scope=None, strip_scope=False): | |
| """Returns a list of variables to restore for the specified list of methods. | |
| It is supposed that variable name starts with the method's scope (a prefix | |
| returned by _method_scope function). | |
| Args: | |
| methods_names: a list of names of configurable methods. | |
| strip_scope: if True will return variable names without method's scope. | |
| If methods_names is None will return names unchanged. | |
| model_scope: a scope for a whole model. | |
| Returns: | |
| a dictionary mapping variable names to variables for restore. | |
| """ | |
| if scope: | |
| variable_map = {} | |
| method_variables = slim.get_variables_to_restore(include=[scope]) | |
| for var in method_variables: | |
| if strip_scope: | |
| var_name = var.op.name[len(scope) + 1:] | |
| else: | |
| var_name = var.op.name | |
| variable_map[var_name] = var | |
| return variable_map | |
| else: | |
| return {v.op.name: v for v in slim.get_variables_to_restore()} | |