sergeyprokudin

import numpy as np


def count_conv_params_flops(conv_layer, verbose=1):
    # out shape is  n_cells_dim1 * (n_cells_dim2 * n_cells_dim3)
    out_shape = conv_layer.output.shape.as_list()
    n_cells_total = np.prod(out_shape[1:-1])

    n_conv_params_total = conv_layer.count_params()

05jd

"""Code Snippet for counting FLOPs of a model. 

Not final version, it will be updated to improve the usability.

"""

import os.path
import tempfile

import tensorflow as tf

jeremyjordan

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,

RyanAkilos

import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix

#Start
train_data_path = 'F://data//Train'

codingPingjun

import pickle
import numpy as np
import pdb

img_width, img_height = 300, 300
box_configs = [
    {'layer_width': 38, 'layer_height': 38, 'num_prior': 3, 'min_size':  30.0,
     'max_size': None, 'aspect_ratios': [1.0, 2.0, 1/2.0]},
    {'layer_width': 19, 'layer_height': 19, 'num_prior': 6, 'min_size':  60.0,
     'max_size': 114.0, 'aspect_ratios': [1.0, 1.0, 2.0, 1/2.0, 3.0, 1/3.0]},

fmder

import numpy
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

cadrev

'''
Title  : Pandas Row Shuffler 
Author : Felan Carlo Garcia
'''
import numpy as np
import pandas as pd

def shuffler(filename):
  df = pd.read_csv(filename, header=0)
  # return the pandas dataframe