# -*- coding: utf-8 -*-
"""
Multinomial logistic regression on MNIST.
- no regularization
- weights and biases initialized to 0
"""
import tensorflow as tf
import mnist_input
[docs]class set_up:
"""Class providing the functionality for multinomial logistic regression on `MNIST`.
The model uses no regularization. All weights and biasses are initialized to be ``0``.
Args:
batch_size (int): Batch size of the data points. No default value specified.
weight_decay (float): Weight decay factor. In this model there is no weight decay implemented. Defaults to ``None``.
Attributes:
data_loading (deepobs.data_loading): Data loading class for `MNIST`, :class:`.mnist_input.data_loading`.
losses (tf.Tensor): Tensor of size ``batch_size`` containing the individual losses per data point.
accuracy (tf.Tensor): Tensor containing the accuracy of the model.
train_init_op (tf.Operation): A TensorFlow operation to be performed before starting every training epoch.
train_eval_init_op (tf.Operation): A TensorFlow operation to be performed before starting every training eval epoch.
test_init_op (tf.Operation): A TensorFlow operation to be performed before starting every test evaluation phase.
"""
def __init__(self, batch_size, weight_decay=None):
"""Initializes the problem set_up class.
Args:
batch_size (int): Batch size of the data points. No default value specified.
weight_decay (float): Weight decay factor. In this model there is no weight decay implemented. Defaults to ``None``.
"""
self.data_loading = mnist_input.data_loading(batch_size=batch_size)
self.losses, self.accuracy = self.set_up(weight_decay)
# Operations to do when switching the phase (the one defined in data_loading initializes the iterator and assigns the phase variable, here you can add more operations)
self.train_init_op = tf.group([self.data_loading.train_init_op])
self.train_eval_init_op = tf.group([self.data_loading.train_eval_init_op])
self.test_init_op = tf.group([self.data_loading.test_init_op])
[docs] def get(self):
"""Returns the losses and the accuray of the model.
Returns:
tupel: Tupel consisting of the losses and the accuracy.
"""
return self.losses, self.accuracy
[docs] def set_up(self, weight_decay):
"""Sets up the test problem.
Args:
weight_decay (float): Weight decay factor. In this model there is no weight decay implemented. Defaults to ``None``.
Returns:
tupel: Tupel consisting of the losses and the accuracy.
"""
if weight_decay is not None:
print "WARNING: Weight decay is non-zero but no weight decay is used for this model."
X, y, phase = self.data_loading.load()
print "X", X.get_shape()
X_flat = tf.reshape(X, [-1, 784])
print "X_flat", X_flat.get_shape()
W = tf.get_variable("W", [784, 10], initializer=tf.constant_initializer(0.0))
b = tf.get_variable("b", [10], initializer=tf.constant_initializer(0.0))
linear_outputs = tf.matmul(X_flat, W) + b
print "linear_outputs", linear_outputs.get_shape()
losses = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=linear_outputs)
y_pred = tf.argmax(linear_outputs, 1)
y_correct = tf.argmax(y, 1)
correct_prediction = tf.equal(y_pred, y_correct)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return losses, accuracy