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How to use _input method in Molotov

Best Python code snippet using molotov_python

Param.py

Source:Param.py

...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...

resnet.py

Source:resnet.py

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            ...

perceptron.py

Source:perceptron.py

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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