import tensorflow as tf

class MnistNeuralNetwork:

	def __init__(self, mnist, create_model, create_vars = None, create_var_values_for_train = None, create_var_values_for_eval = None):
		# The functions to create the model, any additional variables, and their values
		self.create_model = create_model
		self.create_vars = create_vars
		self.create_var_values_for_train = create_var_values_for_train
		self.create_var_values_for_eval = create_var_values_for_eval

		# Hyperparameters
		self.batch_size = 50
		self.learning_rate = 0.05
		self.iterations = 4000

		# Other constants
		self.input_size = 28 * 28
		self.output_size = 10

		# Read data
		self.mnist = mnist


	def train(self):
		# Inputs
		self.x = tf.placeholder(tf.float32, shape = [None, self.input_size], name = "x")
		self.y_ = tf.placeholder(tf.float32, shape = [None, self.output_size], name = "y_")

		# Output
		if (self.create_vars != None):
			self.vars = self.create_vars()
			self.y = self.create_model(self.x, self.input_size, self.output_size, self.vars)
			var_values_for_train = self.create_var_values_for_train(self.vars)
		else:
			self.vars = None
			self.y = self.create_model(self.x, self.input_size, self.output_size)
			var_values_for_train = {}

		# Cost
		self.cross_entropy = tf.reduce_mean(-tf.reduce_sum(self.y_ * tf.log(self.y), reduction_indices = [1]), name = "cross_entropy")

		# Accuracy
		correct_prediction = tf.equal(tf.argmax(self.y, 1), tf.argmax(self.y_, 1))
		self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name = "accuracy")

		# Initialize variables
		self.sess.run(tf.global_variables_initializer())

		# Training
		train_step = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.cross_entropy)
		for i in range(self.iterations):
			batch = self.mnist.train.next_batch(self.batch_size)
			feed_dict = {self.x: batch[0], self.y_: batch[1]}
			feed_dict.update(var_values_for_train)
			if i % 100 == 0:
				train_accuracy = 100 * self.accuracy.eval(feed_dict = feed_dict)
				print("Step {:05d}: batch training accuracy {:.0f}%".format(i, train_accuracy))
			train_step.run(feed_dict = feed_dict)

	def evaluate(self):
		# Evaluate the network
		if self.create_var_values_for_eval != None:
			var_values_for_eval = self.create_var_values_for_eval(self.vars)
		else:
			var_values_for_eval = {}

		feed_dict = {self.x: self.mnist.test.images, self.y_: self.mnist.test.labels}
		feed_dict.update(var_values_for_eval)
		print("Accuracy: {:.3f}%".format(100 * self.accuracy.eval(feed_dict = feed_dict)))


	def train_and_evaluate(self):
		# Initialize TensorFlow session
		self.sess = tf.Session()
		with self.sess.as_default():
			self.train()
			self.evaluate()

c85e6c3bb3a868e0a6e5bdba7697ca1b6589c701
<mask token>


class MnistNeuralNetwork:

    def __init__(self, mnist, create_model, create_vars=None,
        create_var_values_for_train=None, create_var_values_for_eval=None):
        self.create_model = create_model
        self.create_vars = create_vars
        self.create_var_values_for_train = create_var_values_for_train
        self.create_var_values_for_eval = create_var_values_for_eval
        self.batch_size = 50
        self.learning_rate = 0.05
        self.iterations = 4000
        self.input_size = 28 * 28
        self.output_size = 10
        self.mnist = mnist
    <mask token>

    def evaluate(self):
        if self.create_var_values_for_eval != None:
            var_values_for_eval = self.create_var_values_for_eval(self.vars)
        else:
            var_values_for_eval = {}
        feed_dict = {self.x: self.mnist.test.images, self.y_: self.mnist.
            test.labels}
        feed_dict.update(var_values_for_eval)
        print('Accuracy: {:.3f}%'.format(100 * self.accuracy.eval(feed_dict
            =feed_dict)))
    <mask token>

<mask token>


class MnistNeuralNetwork:

    def __init__(self, mnist, create_model, create_vars=None,
        create_var_values_for_train=None, create_var_values_for_eval=None):
        self.create_model = create_model
        self.create_vars = create_vars
        self.create_var_values_for_train = create_var_values_for_train
        self.create_var_values_for_eval = create_var_values_for_eval
        self.batch_size = 50
        self.learning_rate = 0.05
        self.iterations = 4000
        self.input_size = 28 * 28
        self.output_size = 10
        self.mnist = mnist

    def train(self):
        self.x = tf.placeholder(tf.float32, shape=[None, self.input_size],
            name='x')
        self.y_ = tf.placeholder(tf.float32, shape=[None, self.output_size],
            name='y_')
        if self.create_vars != None:
            self.vars = self.create_vars()
            self.y = self.create_model(self.x, self.input_size, self.
                output_size, self.vars)
            var_values_for_train = self.create_var_values_for_train(self.vars)
        else:
            self.vars = None
            self.y = self.create_model(self.x, self.input_size, self.
                output_size)
            var_values_for_train = {}
        self.cross_entropy = tf.reduce_mean(-tf.reduce_sum(self.y_ * tf.log
            (self.y), reduction_indices=[1]), name='cross_entropy')
        correct_prediction = tf.equal(tf.argmax(self.y, 1), tf.argmax(self.
            y_, 1))
        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.
            float32), name='accuracy')
        self.sess.run(tf.global_variables_initializer())
        train_step = tf.train.GradientDescentOptimizer(self.learning_rate
            ).minimize(self.cross_entropy)
        for i in range(self.iterations):
            batch = self.mnist.train.next_batch(self.batch_size)
            feed_dict = {self.x: batch[0], self.y_: batch[1]}
            feed_dict.update(var_values_for_train)
            if i % 100 == 0:
                train_accuracy = 100 * self.accuracy.eval(feed_dict=feed_dict)
                print('Step {:05d}: batch training accuracy {:.0f}%'.format
                    (i, train_accuracy))
            train_step.run(feed_dict=feed_dict)

    def evaluate(self):
        if self.create_var_values_for_eval != None:
            var_values_for_eval = self.create_var_values_for_eval(self.vars)
        else:
            var_values_for_eval = {}
        feed_dict = {self.x: self.mnist.test.images, self.y_: self.mnist.
            test.labels}
        feed_dict.update(var_values_for_eval)
        print('Accuracy: {:.3f}%'.format(100 * self.accuracy.eval(feed_dict
            =feed_dict)))
    <mask token>

<mask token>


class MnistNeuralNetwork:

    def __init__(self, mnist, create_model, create_vars=None,
        create_var_values_for_train=None, create_var_values_for_eval=None):
        self.create_model = create_model
        self.create_vars = create_vars
        self.create_var_values_for_train = create_var_values_for_train
        self.create_var_values_for_eval = create_var_values_for_eval
        self.batch_size = 50
        self.learning_rate = 0.05
        self.iterations = 4000
        self.input_size = 28 * 28
        self.output_size = 10
        self.mnist = mnist

    def train(self):
        self.x = tf.placeholder(tf.float32, shape=[None, self.input_size],
            name='x')
        self.y_ = tf.placeholder(tf.float32, shape=[None, self.output_size],
            name='y_')
        if self.create_vars != None:
            self.vars = self.create_vars()
            self.y = self.create_model(self.x, self.input_size, self.
                output_size, self.vars)
            var_values_for_train = self.create_var_values_for_train(self.vars)
        else:
            self.vars = None
            self.y = self.create_model(self.x, self.input_size, self.
                output_size)
            var_values_for_train = {}
        self.cross_entropy = tf.reduce_mean(-tf.reduce_sum(self.y_ * tf.log
            (self.y), reduction_indices=[1]), name='cross_entropy')
        correct_prediction = tf.equal(tf.argmax(self.y, 1), tf.argmax(self.
            y_, 1))
        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.
            float32), name='accuracy')
        self.sess.run(tf.global_variables_initializer())
        train_step = tf.train.GradientDescentOptimizer(self.learning_rate
            ).minimize(self.cross_entropy)
        for i in range(self.iterations):
            batch = self.mnist.train.next_batch(self.batch_size)
            feed_dict = {self.x: batch[0], self.y_: batch[1]}
            feed_dict.update(var_values_for_train)
            if i % 100 == 0:
                train_accuracy = 100 * self.accuracy.eval(feed_dict=feed_dict)
                print('Step {:05d}: batch training accuracy {:.0f}%'.format
                    (i, train_accuracy))
            train_step.run(feed_dict=feed_dict)

    def evaluate(self):
        if self.create_var_values_for_eval != None:
            var_values_for_eval = self.create_var_values_for_eval(self.vars)
        else:
            var_values_for_eval = {}
        feed_dict = {self.x: self.mnist.test.images, self.y_: self.mnist.
            test.labels}
        feed_dict.update(var_values_for_eval)
        print('Accuracy: {:.3f}%'.format(100 * self.accuracy.eval(feed_dict
            =feed_dict)))

    def train_and_evaluate(self):
        self.sess = tf.Session()
        with self.sess.as_default():
            self.train()
            self.evaluate()

import tensorflow as tf


class MnistNeuralNetwork:

    def __init__(self, mnist, create_model, create_vars=None,
        create_var_values_for_train=None, create_var_values_for_eval=None):
        self.create_model = create_model
        self.create_vars = create_vars
        self.create_var_values_for_train = create_var_values_for_train
        self.create_var_values_for_eval = create_var_values_for_eval
        self.batch_size = 50
        self.learning_rate = 0.05
        self.iterations = 4000
        self.input_size = 28 * 28
        self.output_size = 10
        self.mnist = mnist

    def train(self):
        self.x = tf.placeholder(tf.float32, shape=[None, self.input_size],
            name='x')
        self.y_ = tf.placeholder(tf.float32, shape=[None, self.output_size],
            name='y_')
        if self.create_vars != None:
            self.vars = self.create_vars()
            self.y = self.create_model(self.x, self.input_size, self.
                output_size, self.vars)
            var_values_for_train = self.create_var_values_for_train(self.vars)
        else:
            self.vars = None
            self.y = self.create_model(self.x, self.input_size, self.
                output_size)
            var_values_for_train = {}
        self.cross_entropy = tf.reduce_mean(-tf.reduce_sum(self.y_ * tf.log
            (self.y), reduction_indices=[1]), name='cross_entropy')
        correct_prediction = tf.equal(tf.argmax(self.y, 1), tf.argmax(self.
            y_, 1))
        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.
            float32), name='accuracy')
        self.sess.run(tf.global_variables_initializer())
        train_step = tf.train.GradientDescentOptimizer(self.learning_rate
            ).minimize(self.cross_entropy)
        for i in range(self.iterations):
            batch = self.mnist.train.next_batch(self.batch_size)
            feed_dict = {self.x: batch[0], self.y_: batch[1]}
            feed_dict.update(var_values_for_train)
            if i % 100 == 0:
                train_accuracy = 100 * self.accuracy.eval(feed_dict=feed_dict)
                print('Step {:05d}: batch training accuracy {:.0f}%'.format
                    (i, train_accuracy))
            train_step.run(feed_dict=feed_dict)

    def evaluate(self):
        if self.create_var_values_for_eval != None:
            var_values_for_eval = self.create_var_values_for_eval(self.vars)
        else:
            var_values_for_eval = {}
        feed_dict = {self.x: self.mnist.test.images, self.y_: self.mnist.
            test.labels}
        feed_dict.update(var_values_for_eval)
        print('Accuracy: {:.3f}%'.format(100 * self.accuracy.eval(feed_dict
            =feed_dict)))

    def train_and_evaluate(self):
        self.sess = tf.Session()
        with self.sess.as_default():
            self.train()
            self.evaluate()

import tensorflow as tf

class MnistNeuralNetwork:

	def __init__(self, mnist, create_model, create_vars = None, create_var_values_for_train = None, create_var_values_for_eval = None):
		# The functions to create the model, any additional variables, and their values
		self.create_model = create_model
		self.create_vars = create_vars
		self.create_var_values_for_train = create_var_values_for_train
		self.create_var_values_for_eval = create_var_values_for_eval

		# Hyperparameters
		self.batch_size = 50
		self.learning_rate = 0.05
		self.iterations = 4000

		# Other constants
		self.input_size = 28 * 28
		self.output_size = 10

		# Read data
		self.mnist = mnist


	def train(self):
		# Inputs
		self.x = tf.placeholder(tf.float32, shape = [None, self.input_size], name = "x")
		self.y_ = tf.placeholder(tf.float32, shape = [None, self.output_size], name = "y_")

		# Output
		if (self.create_vars != None):
			self.vars = self.create_vars()
			self.y = self.create_model(self.x, self.input_size, self.output_size, self.vars)
			var_values_for_train = self.create_var_values_for_train(self.vars)
		else:
			self.vars = None
			self.y = self.create_model(self.x, self.input_size, self.output_size)
			var_values_for_train = {}

		# Cost
		self.cross_entropy = tf.reduce_mean(-tf.reduce_sum(self.y_ * tf.log(self.y), reduction_indices = [1]), name = "cross_entropy")

		# Accuracy
		correct_prediction = tf.equal(tf.argmax(self.y, 1), tf.argmax(self.y_, 1))
		self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name = "accuracy")

		# Initialize variables
		self.sess.run(tf.global_variables_initializer())

		# Training
		train_step = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.cross_entropy)
		for i in range(self.iterations):
			batch = self.mnist.train.next_batch(self.batch_size)
			feed_dict = {self.x: batch[0], self.y_: batch[1]}
			feed_dict.update(var_values_for_train)
			if i % 100 == 0:
				train_accuracy = 100 * self.accuracy.eval(feed_dict = feed_dict)
				print("Step {:05d}: batch training accuracy {:.0f}%".format(i, train_accuracy))
			train_step.run(feed_dict = feed_dict)

	def evaluate(self):
		# Evaluate the network
		if self.create_var_values_for_eval != None:
			var_values_for_eval = self.create_var_values_for_eval(self.vars)
		else:
			var_values_for_eval = {}

		feed_dict = {self.x: self.mnist.test.images, self.y_: self.mnist.test.labels}
		feed_dict.update(var_values_for_eval)
		print("Accuracy: {:.3f}%".format(100 * self.accuracy.eval(feed_dict = feed_dict)))


	def train_and_evaluate(self):
		# Initialize TensorFlow session
		self.sess = tf.Session()
		with self.sess.as_default():
			self.train()
			self.evaluate()
