<commit_before>#!/usr/bin/python

# General Packages
import os
import sys
import time
import pdb
from collections import OrderedDict

# Math Packages
import math
import numpy
import cv2

# Theano Packages
import theano
import theano.tensor as T
from theano.ifelse import ifelse

# CNN code packages
from cnn import ReLU
from cnn import Sigmoid
from cnn import Tanh
from cnn import SVMLayer
from cnn import LogisticRegression
from cnn import HiddenLayer
from cnn import _dropout_from_layer
from cnn import DropoutHiddenLayer
from cnn import MLP
from cnn import LeNetConvPoolLayer

# Utilities
from util import visualize
from util import visualize_color_filters
from util import save_network
from util import load_network

# Datahandling packages
from loaders import load_data_pkl
from loaders import load_data_mat
from loaders import load_skdata_caltech101
from loaders import load_skdata_mnist
from loaders import load_skdata_cifar10
from loaders import load_skdata_mnist_noise1
from loaders import load_skdata_mnist_noise2
from loaders import load_skdata_mnist_noise3
from loaders import load_skdata_mnist_noise4
from loaders import load_skdata_mnist_noise5
from loaders import load_skdata_mnist_noise6
from loaders import load_skdata_mnist_bg_images
from loaders import load_skdata_mnist_bg_rand
from loaders import load_skdata_mnist_rotated
from loaders import load_skdata_mnist_rotated_bg



def run ( 
                   filename_params         ,                  
                   verbose                 ,                                                # True prints in a lot of intermetediate steps, False keeps it to minimum.
				   data_params			   ,
                   visual_params           
        ):


    #####################
    # Unpack Variables  #
    #####################
        
        
   
    visualize_flag          = visual_params ["visualize_flag" ]
    n_visual_images         = visual_params ["n_visual_images" ] 
    display_flag            = visual_params ["display_flag" ]           
                
    results_file_name   = filename_params [ "results_file_name" ]                # Files that will be saved down on completion Can be used by the parse.m file
    confusion_file_name = filename_params [ "confusion_file_name" ]
    load_file_name      = filename_params [ "load_file_name" ]
     
    dataset             = data_params [ "loc" ]
    height              = data_params [ "height" ]
    width               = data_params [ "width" ]
    batch_size          = data_params [ "batch_size" ]    
    load_batches        = data_params [ "load_batches"  ] * batch_size
    batches2test        = data_params [ "batches2test" ]
    channels            = data_params [ "channels" ]
             
                
    #################
    # Load Network  #
    #################            
    params, arch_params = load_network('network.pkl.gz')                         
                
    squared_filter_length_limit     = arch_params [ "squared_filter_length_limit" ]   
    n_epochs                        = arch_params [ "n_epochs" ]
    validate_after_epochs           = arch_params [ "validate_after_epochs"  ]
    mlp_activations                 = arch_params [ "mlp_activations"  ] 
    cnn_activations                 = arch_params [ "cnn_activations" ]
    dropout                         = arch_params [ "dropout"  ]
    column_norm                     = arch_params [ "column_norm"  ]    
    dropout_rates                   = arch_params [ "dropout_rates" ]
    nkerns                          = arch_params [ "nkerns"  ]
    outs                            = arch_params [ "outs" ]<commit_msg>some commit. Going to revert<commit_after>#!/usr/bin/python

# General Packages
import os
import sys
import time
import pdb
from collections import OrderedDict

# Math Packages
import math
import numpy
import cv2

# Theano Packages
import theano
import theano.tensor as T
from theano.ifelse import ifelse

# CNN code packages
from cnn import ReLU
from cnn import Sigmoid
from cnn import Tanh
from cnn import SVMLayer
from cnn import LogisticRegression
from cnn import HiddenLayer
from cnn import _dropout_from_layer
from cnn import DropoutHiddenLayer
from cnn import MLP
from cnn import LeNetConvPoolLayer

# Utilities
from util import visualize
from util import visualize_color_filters
from util import save_network
from util import load_network

# Datahandling packages
from loaders import load_data_pkl
from loaders import load_data_mat
from loaders import load_skdata_caltech101
from loaders import load_skdata_mnist
from loaders import load_skdata_cifar10
from loaders import load_skdata_mnist_noise1
from loaders import load_skdata_mnist_noise2
from loaders import load_skdata_mnist_noise3
from loaders import load_skdata_mnist_noise4
from loaders import load_skdata_mnist_noise5
from loaders import load_skdata_mnist_noise6
from loaders import load_skdata_mnist_bg_images
from loaders import load_skdata_mnist_bg_rand
from loaders import load_skdata_mnist_rotated
from loaders import load_skdata_mnist_rotated_bg



def run ( 
                   filename_params         ,                  
                   verbose                 ,                                                # True prints in a lot of intermetediate steps, False keeps it to minimum.
				   data_params			   ,
                   visual_params           
        ):


    #####################
    # Unpack Variables  #
    #####################
        
        
   
    visualize_flag          = visual_params ["visualize_flag" ]
    n_visual_images         = visual_params ["n_visual_images" ] 
    display_flag            = visual_params ["display_flag" ]           
                
    results_file_name   = filename_params [ "results_file_name" ]                # Files that will be saved down on completion Can be used by the parse.m file
    confusion_file_name = filename_params [ "confusion_file_name" ]
    load_file_name      = filename_params [ "load_file_name" ]
     
    dataset             = data_params [ "loc" ]
    height              = data_params [ "height" ]
    width               = data_params [ "width" ]
    batch_size          = data_params [ "batch_size" ]    
    load_batches        = data_params [ "load_batches"  ] * batch_size
    batches2test        = data_params [ "batches2test" ]
    channels            = data_params [ "channels" ]
             
                
    #################
    # Load Network  #
    #################            
    params, arch_params = load_network(load_file_name)                         
                
    squared_filter_length_limit     = arch_params [ "squared_filter_length_limit" ]   
    n_epochs                        = arch_params [ "n_epochs" ]
    validate_after_epochs           = arch_params [ "validate_after_epochs"  ]
    mlp_activations                 = arch_params [ "mlp_activations"  ] 
    cnn_activations                 = arch_params [ "cnn_activations" ]
    dropout                         = arch_params [ "dropout"  ]
    column_norm                     = arch_params [ "column_norm"  ]    
    dropout_rates                   = arch_params [ "dropout_rates" ]
    nkerns                          = arch_params [ "nkerns"  ]
    outs                            = arch_params [ "outs" ]
{'fraction_non_alphanumeric': 0.06269635295724628, 'fraction_numerical': 0.0038246141237535856, 'mean_word_length': 2.200174825174825, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5485954', 'n_tokens_mistral': 2114, 'n_tokens_neox': 2012, 'n_words': 540}