Python download_data Examples

Example #1

File: model_1.0.1.39n.bu.py Project: saidbm24/Convnet-for-Mammogram-analysis

import numpy as np
import os
import wget
from sklearn.model_selection import train_test_split
import tensorflow as tf
from training_utils import download_file, get_batches, read_and_decode_single_example, load_validation_data, \
    download_data, evaluate_model, get_training_data
import argparse
from tensorboard import summary as summary_lib

dataset = 5

# download the data
download_data(what=dataset)
# ## Create Model

# config
# If number of epochs has been passed in use that, otherwise default to 50
parser = argparse.ArgumentParser()
parser.add_argument("-e", "--epochs", help="number of epochs to train", type=int)
args = parser.parse_args()

if args.epochs:
    epochs = args.epochs
else:
    epochs = 50

batch_size = 64

train_files, total_records = get_training_data(what=dataset)

Example #2

File: candidate_1.1.0.40.py Project: saidbm24/Convnet-for-Mammogram-analysis

import numpy as np
import os
import wget
from sklearn.model_selection import train_test_split
import tensorflow as tf
from training_utils import download_file, get_batches, read_and_decode_single_example, load_validation_data, \
    download_data, evaluate_model, get_training_data
import argparse
from tensorboard import summary as summary_lib

# download the data
download_data()
# ## Create Model

# config
# If number of epochs has been passed in use that, otherwise default to 50
parser = argparse.ArgumentParser()
parser.add_argument("-e", "--epochs", help="number of epochs to train", type=int)
args = parser.parse_args()

if args.epochs:
    epochs = args.epochs
else:
    epochs = 50

batch_size = 64

train_files, total_records = get_training_data(type="new")

## Hyperparameters
# Small epsilon value for the BN transform

Example #3

import numpy as np
import os
import wget
from sklearn.cross_validation import train_test_split
import tensorflow as tf
from training_utils import download_file, get_batches, read_and_decode_single_example, load_validation_data, \
    download_data, evaluate_model, get_training_data
import sys
import argparse
from tensorboard import summary as summary_lib

# download the data
download_data(what="old")
# ## Create Model

## config
# If number of epochs has been passed in use that, otherwise default to 50
parser = argparse.ArgumentParser()
parser.add_argument("-e",
                    "--epochs",
                    help="number of epochs to train",
                    type=int)
args = parser.parse_args()

if args.epochs:
    epochs = args.epochs
else:
    epochs = 50

batch_size = 64

Example #4

File: model_1.0.0.24.py Project: saidbm24/Convnet-for-Mammogram-analysis

import numpy as np
import os
import wget
from sklearn.model_selection import train_test_split
import tensorflow as tf
from training_utils import download_file, get_batches, read_and_decode_single_example, load_validation_data, \
    download_data, evaluate_model, get_training_data
import sys
import argparse
from tensorboard import summary as summary_lib

# download the data
download_data(what=5)
# ## Create Model

## config
# If number of epochs has been passed in use that, otherwise default to 50
parser = argparse.ArgumentParser()
parser.add_argument("-e",
                    "--epochs",
                    help="number of epochs to train",
                    type=int)
args = parser.parse_args()

if args.epochs:
    epochs = args.epochs
else:
    epochs = 50

batch_size = 64

Example #5

File: candidate_1.3.1.01n.py Project: saidbm24/Convnet-for-Mammogram-analysis

import numpy as np
import os
import wget
from sklearn.model_selection import train_test_split
import tensorflow as tf
from training_utils import download_file, get_batches, read_and_decode_single_example, load_validation_data, \
    download_data, evaluate_model, get_training_data, _conv2d_batch_norm, _dense_batch_norm
import argparse
from tensorboard import summary as summary_lib

# download the data
download_data(what=6)

## config
# If number of epochs has been passed in use that, otherwise default to 50
parser = argparse.ArgumentParser()
parser.add_argument("-e",
                    "--epochs",
                    help="number of epochs to train",
                    type=int)
args = parser.parse_args()

if args.epochs:
    epochs = args.epochs
else:
    epochs = 50

# set the batch size
batch_size = 64

train_files, total_records = get_training_data(what=6)