import tensorflow as tf


def weight_variable(shape,var_name):
	temp_var = tf.truncated_normal(shape,stddev=0.1)
	return tf.Variable(temp_var,name=var_name)

def bias_variable(shape,var_name):
	temp_var = tf.constant(0.1,shape=shape)
	return tf.Variable(temp_var,name=var_name)

def full_layer(input_vector,size,prefix_name,activation="relu"):
	in_size = int(input_vector.get_shape()[1])
	W = weight_variable([in_size,size],prefix_name + "_dense_W")
	b = bias_variable([size],prefix_name+"_dense_b")
	if activation == "relu":
		return tf.nn.relu(tf.matmul(input_vector,W) + b)
	elif activation == "sigmoid":
		return tf.nn.sigmoid(tf.matmul(input_vector,W) + b)
	elif activation == "None":
		return tf.matmul(input_vector,W)+b
f84ab26da1ca0a746ff6948380ecb95e431e24cc
<mask token>


def full_layer(input_vector, size, prefix_name, activation='relu'):
    in_size = int(input_vector.get_shape()[1])
    W = weight_variable([in_size, size], prefix_name + '_dense_W')
    b = bias_variable([size], prefix_name + '_dense_b')
    if activation == 'relu':
        return tf.nn.relu(tf.matmul(input_vector, W) + b)
    elif activation == 'sigmoid':
        return tf.nn.sigmoid(tf.matmul(input_vector, W) + b)
    elif activation == 'None':
        return tf.matmul(input_vector, W) + b

<mask token>


def bias_variable(shape, var_name):
    temp_var = tf.constant(0.1, shape=shape)
    return tf.Variable(temp_var, name=var_name)


def full_layer(input_vector, size, prefix_name, activation='relu'):
    in_size = int(input_vector.get_shape()[1])
    W = weight_variable([in_size, size], prefix_name + '_dense_W')
    b = bias_variable([size], prefix_name + '_dense_b')
    if activation == 'relu':
        return tf.nn.relu(tf.matmul(input_vector, W) + b)
    elif activation == 'sigmoid':
        return tf.nn.sigmoid(tf.matmul(input_vector, W) + b)
    elif activation == 'None':
        return tf.matmul(input_vector, W) + b

<mask token>


def weight_variable(shape, var_name):
    temp_var = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(temp_var, name=var_name)


def bias_variable(shape, var_name):
    temp_var = tf.constant(0.1, shape=shape)
    return tf.Variable(temp_var, name=var_name)


def full_layer(input_vector, size, prefix_name, activation='relu'):
    in_size = int(input_vector.get_shape()[1])
    W = weight_variable([in_size, size], prefix_name + '_dense_W')
    b = bias_variable([size], prefix_name + '_dense_b')
    if activation == 'relu':
        return tf.nn.relu(tf.matmul(input_vector, W) + b)
    elif activation == 'sigmoid':
        return tf.nn.sigmoid(tf.matmul(input_vector, W) + b)
    elif activation == 'None':
        return tf.matmul(input_vector, W) + b

import tensorflow as tf


def weight_variable(shape, var_name):
    temp_var = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(temp_var, name=var_name)


def bias_variable(shape, var_name):
    temp_var = tf.constant(0.1, shape=shape)
    return tf.Variable(temp_var, name=var_name)


def full_layer(input_vector, size, prefix_name, activation='relu'):
    in_size = int(input_vector.get_shape()[1])
    W = weight_variable([in_size, size], prefix_name + '_dense_W')
    b = bias_variable([size], prefix_name + '_dense_b')
    if activation == 'relu':
        return tf.nn.relu(tf.matmul(input_vector, W) + b)
    elif activation == 'sigmoid':
        return tf.nn.sigmoid(tf.matmul(input_vector, W) + b)
    elif activation == 'None':
        return tf.matmul(input_vector, W) + b

import tensorflow as tf


def weight_variable(shape,var_name):
	temp_var = tf.truncated_normal(shape,stddev=0.1)
	return tf.Variable(temp_var,name=var_name)

def bias_variable(shape,var_name):
	temp_var = tf.constant(0.1,shape=shape)
	return tf.Variable(temp_var,name=var_name)

def full_layer(input_vector,size,prefix_name,activation="relu"):
	in_size = int(input_vector.get_shape()[1])
	W = weight_variable([in_size,size],prefix_name + "_dense_W")
	b = bias_variable([size],prefix_name+"_dense_b")
	if activation == "relu":
		return tf.nn.relu(tf.matmul(input_vector,W) + b)
	elif activation == "sigmoid":
		return tf.nn.sigmoid(tf.matmul(input_vector,W) + b)
	elif activation == "None":
		return tf.matmul(input_vector,W)+b