# 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.
# ==============================================================================

"""Convolutional Model class.

Uses only convolutional and fully connected layers for the inference ops.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from models import model
from models.layers import variables


class ConvModel(model.Model):
  """A baseline multi GPU Model without capsule layers.

  The inference graph includes ReLU convolution layers and fully connected
  layers. The last layer is linear and has 10 units.
  """

  def _add_convs(self, input_tensor, channels):
    """Adds the convolution layers.

    Adds a series of convolution layers with ReLU nonlinearity and pooling
    after each of them.

    Args:
      input_tensor: a 4D float tensor as the input to the first convolution.
      channels: A list of channel sizes for input_tensor and following
        convolution layers. Number of channels in input tensor should be
        equal to channels[0].
    Returns:
      A 4D tensor as the output of the last pooling layer.
    """
    for i in xrange(1, len(channels)):
      with tf.variable_scope('conv{}'.format(i)) as scope:
        kernel = variables.weight_variable(
            shape=[5, 5, channels[i - 1], channels[i]], stddev=5e-2,
            verbose=self._hparams.verbose
        )
        conv = tf.nn.conv2d(
            input_tensor,
            kernel, [1, 1, 1, 1],
            padding=self._hparams.padding,
            data_format='NCHW')
        biases = variables.bias_variable([channels[i]],
                                         verbose=self._hparams.verbose)
        pre_activation = tf.nn.bias_add(conv, biases, data_format='NCHW')
        relu = tf.nn.relu(pre_activation, name=scope.name)
        if self._hparams.verbose:
          tf.summary.histogram('activation', relu)
        input_tensor = tf.contrib.layers.max_pool2d(
            relu, kernel_size=2, stride=2, data_format='NCHW', padding='SAME')

    return input_tensor

  def inference(self, features):
    """Adds the inference graph ops.

    Builds the architecture of the neural net to drive logits from features.
    The inference graph includes a series of convolution and fully connected
    layers and outputs a [batch, 10] tensor as the logits.

    Args:
      features: Dictionary of batched feature tensors like images and labels.
    Returns:
      A model.Inferred named tuple of expected outputs of the model like
      'logits' and 'remakes' for the reconstructions (to be added).
    """
    image = features['images']
    image_dim = features['height']
    image_depth = features['depth']
    image_4d = tf.reshape(image, [-1, image_depth, image_dim, image_dim])
    conv = self._add_convs(image_4d, [image_depth, 512, 256])
    hidden1 = tf.contrib.layers.flatten(conv)

    with tf.variable_scope('fc1') as scope:
      dim = hidden1.get_shape()[1].value
      weights = variables.weight_variable(shape=[dim, 1024], stddev=0.1,
                                          verbose=self._hparams.verbose)
      biases = variables.bias_variable(shape=[1024],
                                       verbose=self._hparams.verbose)
      pre_activation = tf.matmul(hidden1, weights) + biases
      hidden2 = tf.nn.relu(pre_activation, name=scope.name)

    with tf.variable_scope('softmax_layer') as scope:
      weights = variables.weight_variable(
          shape=[1024, features['num_classes']], stddev=0.1,
          verbose=self._hparams.verbose
      )
      biases = variables.bias_variable(shape=[features['num_classes']],
                                       verbose=self._hparams.verbose)
      logits = tf.matmul(hidden2, weights) + biases

    return model.Inferred(logits, None)

# 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.
# ==============================================================================

"""Convolutional Model class.

Uses only convolutional and fully connected layers for the inference ops.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from models import model
from models.layers import variables


class ConvModel(model.Model):
  """A baseline multi GPU Model without capsule layers.

  The inference graph includes ReLU convolution layers and fully connected
  layers. The last layer is linear and has 10 units.
  """

  def _add_convs(self, input_tensor, channels):
    """Adds the convolution layers.

    Adds a series of convolution layers with ReLU nonlinearity and pooling
    after each of them.

    Args:
      input_tensor: a 4D float tensor as the input to the first convolution.
      channels: A list of channel sizes for input_tensor and following
        convolution layers. Number of channels in input tensor should be
        equal to channels[0].
    Returns:
      A 4D tensor as the output of the last pooling layer.
    """
    for i in xrange(1, len(channels)):
      with tf.variable_scope('conv{}'.format(i)) as scope:
        kernel = variables.weight_variable(
            shape=[5, 5, channels[i - 1], channels[i]], stddev=5e-2,
            verbose=self._hparams.verbose
        )
        conv = tf.nn.conv2d(
            input_tensor,
            kernel, [1, 1, 1, 1],
            padding=self._hparams.padding,
            data_format='NCHW')
        biases = variables.bias_variable([channels[i]],
                                         verbose=self._hparams.verbose)
        pre_activation = tf.nn.bias_add(conv, biases, data_format='NCHW')
        relu = tf.nn.relu(pre_activation, name=scope.name)
        if self._hparams.verbose:
          tf.summary.histogram('activation', relu)
        input_tensor = tf.contrib.layers.max_pool2d(
            relu, kernel_size=2, stride=2, data_format='NCHW', padding='SAME')

    return input_tensor

  def inference(self, features):
    """Adds the inference graph ops.

    Builds the architecture of the neural net to drive logits from features.
    The inference graph includes a series of convolution and fully connected
    layers and outputs a [batch, 10] tensor as the logits.

    Args:
      features: Dictionary of batched feature tensors like images and labels.
    Returns:
      A model.Inferred named tuple of expected outputs of the model like
      'logits' and 'remakes' for the reconstructions (to be added).
    """
    image = features['images']
    image_dim = features['height']
    image_depth = features['depth']
    image_4d = tf.reshape(image, [-1, image_depth, image_dim, image_dim])
    conv = self._add_convs(image_4d, [image_depth, 512, 256])
    hidden1 = tf.contrib.layers.flatten(conv)

    with tf.variable_scope('fc1') as scope:
      dim = hidden1.get_shape()[1].value
      weights = variables.weight_variable(shape=[dim, 1024], stddev=0.1,
                                          verbose=self._hparams.verbose)
      biases = variables.bias_variable(shape=[1024],
                                       verbose=self._hparams.verbose)
      pre_activation = tf.matmul(hidden1, weights) + biases
      hidden2 = tf.nn.relu(pre_activation, name=scope.name)

    with tf.variable_scope('softmax_layer') as scope:
      weights = variables.weight_variable(
          shape=[1024, features['num_classes']], stddev=0.1,
          verbose=self._hparams.verbose
      )
      biases = variables.bias_variable(shape=[features['num_classes']],
                                       verbose=self._hparams.verbose)
      logits = tf.matmul(hidden2, weights) + biases

    return model.Inferred(logits, None)

# 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. # ============================================================================== Convolutional Model class.  Uses only convolutional and fully connected layers for the inference ops. A baseline multi GPU Model without capsule layers.    The inference graph includes ReLU convolution layers and fully connected   layers. The last layer is linear and has 10 units. Adds the convolution layers.      Adds a series of convolution layers with ReLU nonlinearity and pooling     after each of them.      Args:       input_tensor: a 4D float tensor as the input to the first convolution.       channels: A list of channel sizes for input_tensor and following         convolution layers. Number of channels in input tensor should be         equal to channels[0].     Returns:       A 4D tensor as the output of the last pooling layer. Adds the inference graph ops.      Builds the architecture of the neural net to drive logits from features.     The inference graph includes a series of convolution and fully connected     layers and outputs a [batch, 10] tensor as the logits.      Args:       features: Dictionary of batched feature tensors like images and labels.     Returns:       A model.Inferred named tuple of expected outputs of the model like       'logits' and 'remakes' for the reconstructions (to be added).