import tensorflow as tf

def add_layer(inputs,in_size,out_size,activation_function=None):
	with tf.name_scope('layer'):
		with tf.name_scope('weights'):
			weights=tf.Variable(tf.random_normal([in_size,out_size]),name='w')
		with tf.name_scope('bias'):
			bias=tf.Variable(tf.zeros([1,out_size])+0.1,name='b')
		with tf.name_scope('Wx_plus_b'):
			Wx_plus_b=tf.add(tf.matmul(inputs,weights),bias)
		if activation_function is None:
			outputs=Wx_plus_b
		else:
			outputs=activation_function(Wx_plus_b,)
		return outputs
with tf.name_scope('inputs'):
	xs=tf.placeholder(tf.float32,[None,1],name='x_input')
	ys=tf.placeholder(tf.float32,[None,1],name='y_input')
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)
with tf.name_scope('loss'):
	loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
with tf.name_scope('train'):
	train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess=tf.Session

init=tf.initialize_all_variables()
sess.run(init)

b55a9a5e3f9f3d57295c932172d65c434a832d40
<mask token>


def add_layer(inputs, in_size, out_size, activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            weights = tf.Variable(tf.random_normal([in_size, out_size]),
                name='w')
        with tf.name_scope('bias'):
            bias = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, weights), bias)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs


<mask token>

<mask token>


def add_layer(inputs, in_size, out_size, activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            weights = tf.Variable(tf.random_normal([in_size, out_size]),
                name='w')
        with tf.name_scope('bias'):
            bias = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, weights), bias)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs


with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')
<mask token>
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
        reduction_indices=[1]))
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
<mask token>
sess.run(init)

<mask token>


def add_layer(inputs, in_size, out_size, activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            weights = tf.Variable(tf.random_normal([in_size, out_size]),
                name='w')
        with tf.name_scope('bias'):
            bias = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, weights), bias)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs


with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function=None)
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
        reduction_indices=[1]))
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session
init = tf.initialize_all_variables()
sess.run(init)

import tensorflow as tf


def add_layer(inputs, in_size, out_size, activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            weights = tf.Variable(tf.random_normal([in_size, out_size]),
                name='w')
        with tf.name_scope('bias'):
            bias = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, weights), bias)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs


with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function=None)
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
        reduction_indices=[1]))
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session
init = tf.initialize_all_variables()
sess.run(init)

import tensorflow as tf

def add_layer(inputs,in_size,out_size,activation_function=None):
	with tf.name_scope('layer'):
		with tf.name_scope('weights'):
			weights=tf.Variable(tf.random_normal([in_size,out_size]),name='w')
		with tf.name_scope('bias'):
			bias=tf.Variable(tf.zeros([1,out_size])+0.1,name='b')
		with tf.name_scope('Wx_plus_b'):
			Wx_plus_b=tf.add(tf.matmul(inputs,weights),bias)
		if activation_function is None:
			outputs=Wx_plus_b
		else:
			outputs=activation_function(Wx_plus_b,)
		return outputs
with tf.name_scope('inputs'):
	xs=tf.placeholder(tf.float32,[None,1],name='x_input')
	ys=tf.placeholder(tf.float32,[None,1],name='y_input')
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)
with tf.name_scope('loss'):
	loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
with tf.name_scope('train'):
	train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess=tf.Session

init=tf.initialize_all_variables()
sess.run(init)
