How to use _input method in Molotov

Best Python code snippet using molotov_python

Param.py

Source:Param.py Github

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...143#end if"""144class Param(Element):145 """The graphical parameter."""146 def __init__(self): Element.__init__(self)147 def get_input(self, *args, **kwargs):148 """149 Get the graphical gtk class to represent this parameter.150 An enum requires and combo parameter.151 A non-enum with options gets a combined entry/combo parameter.152 All others get a standard entry parameter.153 @return gtk input class154 """155 if self.is_enum(): return EnumParam(self, *args, **kwargs)156 if self.get_options(): return EnumEntryParam(self, *args, **kwargs)157 return EntryParam(self, *args, **kwargs)158 def get_markup(self):159 """160 Get the markup for this param.161 @return a pango markup string...

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resnet.py

Source:resnet.py Github

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1import os2os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 3import tensorflow as tf4tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)5import numpy as np6from .layers import *7# Constructs a graph of resnet block8# Default input is channles last.9def resnet_block(_input,10 isTraining=None,11 channel=128,12 require_bn=False,13 require_in=False,14 dilation_rate=(1,1),15 kernel_size=(3,3),16 data_format="channels_last",17 activation=tf.nn.elu):18 19 # Figuring out channel size20 if channel%2 != 0:21 print("Even number channels are required.")22 return -123 down_channel = int(channel/2)24 25 # Figuring out bn axis26 if data_format=="channels_first":27 bn_axis = 128 elif data_format=="channels_last":29 bn_axis = 330 31 ##############################################32 # Full pre-activation style: #33 # bn -> elu -> conv2d -> bn -> elu -> conv2d #34 # bn -> elu -> conv2d #35 ##############################################36 37 if require_bn: _input = tf.layers.batch_normalization(_input,38 training=isTraining,39 center=True,40 scale=True,41 axis=bn_axis)42 if require_in: _input = tf.contrib.layers.instance_norm(_input)43 44 _input = activation(_input)45 _input = tf.layers.conv2d(_input,46 filters=down_channel,47 kernel_size=(1,1),48 data_format=data_format)49 50 if require_bn: _input = tf.layers.batch_normalization(_input,51 training=isTraining,52 center=True,53 scale=True,54 axis=bn_axis)55 if require_in: _input = tf.contrib.layers.instance_norm(_input)56 57 _input = activation(_input)58 _input = tf.layers.conv2d(_input,59 filters=down_channel, 60 kernel_size=kernel_size, 61 dilation_rate=dilation_rate,62 data_format=data_format,63 padding="same")64 65 if require_bn: _input = tf.layers.batch_normalization(_input,66 training=isTraining,67 center=True,68 scale=True,69 axis=bn_axis)70 if require_in: _input = tf.contrib.layers.instance_norm(_input)71 72 _input = activation(_input)73 _input = tf.layers.conv2d(_input,74 filters=channel, 75 kernel_size=(1,1),76 data_format=data_format)77 return _input78# Creates a resnet architecture.79def build_resnet(_input,80 channel,81 num_chunks,82 isTraining=None,83 require_bn=False, #Batchnorm flag84 require_in=False, #Instancenorm flag85 data_format="channels_last",86 first_projection=True,87 no_last_dilation=False,88 transpose_matrix=False,89 dilation_cycle = [1,2,4,8]):90 91 # Projection of the very first input to 128 channels.92 if first_projection:93 _input = tf.layers.conv2d(_input,94 filters=channel,95 kernel_size=(1,1),96 dilation_rate=(1,1),97 data_format=data_format)98 99 # each chunk contatins 4 blocks with cycling dilation rates.100 for i in range(num_chunks):101 # dilation rates102 for dr in dilation_cycle:103 # save residual connection104 _residual = _input105 # pass through resnet block106 _conved = resnet_block(_input,107 isTraining=isTraining,108 require_bn=require_bn,109 require_in=require_in,110 channel=channel,111 dilation_rate=(dr, dr),112 data_format=data_format)113 # genearte input to the next block114 _input = _residual+_conved115 if transpose_matrix:116 _input = (_input+tf.transpose(_input, [0,2,1,3]))/2117 118 119 # 2 more extra blocks with dilation120 if no_last_dilation:121 for i in range(2):122 _residual = _input123 # pass through resnet block124 _conved = resnet_block(_input,125 isTraining=isTraining,126 require_bn=require_bn,127 require_in=require_in,128 channel=channel,129 dilation_rate=(1, 1),130 data_format=data_format)131 # genearte input to the next block132 _input = _residual+_conved133 if transpose_matrix:134 _input = (_input+tf.transpose(_input, [0,2,1,3]))/2135 ...

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perceptron.py

Source:perceptron.py Github

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1# -*- coding: utf-8 -*-2"""3Created on Wed Jul 7 10:44:16 20214@author: axelt5"""6import numpy as np7def activationFunction_sigmoid (value):8 return 1.0/(1+np.e**(-1*value))9class perceptron (object):10 ACTIVATIONFUNCTIONS = {"sigmoid":activationFunction_sigmoid}11 def __init__(self,weights,_activationFunction="sigmoid"):12 self.activationFunction = perceptron.ACTIVATIONFUNCTIONS[_activationFunction]13 self.weights=weights14 15 def process(self,_input):16 #h = x_1 * w_1 + ... + x_n * w_n17 total = (np.dot(_input,weights))18 #dot product can also be expressed by:19 #total = sum(_input*self.weights)20 return self.activationFunction(total)21 def update (self, _input, target, learningRate=0.4):22 #update rule: w_i = w_i + learning rate * (target-output)*x_i23 for i in range (0, len(self.weights)):24 self.weights[i]=self.weights[i]+learningRate*(target-self.process(_input))*_input[i]25if __name__ == "__main__":26 _input = np.array([5,6,7,8,9], dtype="float")27 target = 128 _input = _input/sum(_input)29 weights = np.random.rand(len(_input))30 print ("Initial Weights",weights)31 print ("Normalisedc Input",_input)32 p = perceptron(weights)33 34 output = p.process(_input)35 print ("Initital Output is ", output)36 37 GENERATIONS = 100038 LEARNINGRATE = 0.339 40 for i in range (0, GENERATIONS):41 p.update(_input,target,LEARNINGRATE)42 output = p.process(_input)43 print ("Trained Output is ", output)...

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