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)
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
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
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
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)