Ejemplos de make_directory en Python

Ejemplo n.º 1

Archivo: train_synthetic_data.py Proyecto: MolGen/learning_sequence_motifs

import os, sys
import numpy as np
import helper
import tensorflow as tf
from deepomics import neuralnetwork as nn
from deepomics import utils, fit

#------------------------------------------------------------------------------------------------

all_models = [
    'cnn_2', 'cnn_4', 'cnn_10', 'cnn_25', 'cnn_50', 'cnn_100', 'cnn_50_2',
    'cnn9_4', 'cnn9_25', 'cnn3_50', 'cnn3_2'
]

# save path
results_path = utils.make_directory('../results', 'synthetic')
params_path = utils.make_directory(results_path, 'model_params')

# load dataset
data_path = '../data/synthetic_dataset.h5'
train, valid, test = helper.load_synthetic_dataset(data_path)

# get data shapes
input_shape = list(train['inputs'].shape)
input_shape[0] = None
output_shape = [None, train['targets'].shape[1]]

# loop through models
for model_name in all_models:
    print('model: ' + model_name)

Ejemplo n.º 2

import helper
np.random.seed(247)
tf.set_random_seed(247)

#---------------------------------------------------------------------------------------------------------

all_models = ['DistNet', 'LocalNet']
dropout_status = [True, False]
l2_status = [True, False]
bn_status = [True, False]
noise_status = [False, True, False]
adv_status = [False, False, True]

# save path
results_path = '../results'
params_path = utils.make_directory(results_path, 'model_params')

# dataset path
data_path = '../data/Synthetic_dataset.h5'
train, valid, test = helper.load_synthetic_dataset(data_path)

# get data shapes
input_shape = list(train['inputs'].shape)
input_shape[0] = None

with open(os.path.join(results_path, 'performance.tsv'), 'wb') as f:

    for i in range(len(dropout_status)):

        for n, noise in enumerate(noise_status):
            # loop through models

Ejemplo n.º 3

Archivo: 1_train_all_models_adv.py Proyecto: robo-sq/robust_features_with_inductive_bias

#-------------------------------------------------------------------------------------------

all_models = ['cnn_deep_noreg'] 
# all adversarial, clean then all adversarial
adv_type = [(80, 0, const_annealing(0)), (80, 20, const_annealing(0)), (80, 20, const_annealing(0.5)), (80, 0, cos_annealing(80))]

batch_size = 50
verbose = 1 

print_adv_test = True

# save path
results_path = '../results'
#results_path = '/content/drive/My Drive/results'
model_path = utils.make_directory(results_path, 'model_params')
metrics_path = utils.make_directory(results_path, 'train_metrics')

# dataset path
data_path = '../data/Synthetic_dataset.h5'
train, valid, test = helper.load_synthetic_dataset(data_path)

adv_test = copy.deepcopy(test)

# get data shapes
input_shape = list(train['inputs'].shape)
input_shape[0] = None
output_shape = [None, train['targets'].shape[1]]

for model_name in all_models:

Ejemplo n.º 4

Archivo: 1_train_all_models_mnist_adv.py Proyecto: robo-sq/robust_features_with_inductive_bias

#-------------------------------------------------------------------------------------------

all_models = ['cnn_25_noreg_mnist'] 
# all adversarial, clean then all adversarial
adv_type = [(40, 0, const_annealing(0)), (40, 10, const_annealing(0)), (40, 10, const_annealing(0.5)), (40, 0, cos_annealing(40))]
#adv_type = [(80, 0, cos_annealing(80))]

batch_size = 50
verbose = 1 
print_adv_test = True

# save path
results_path = '../results'
#results_path = '/content/drive/My Drive/results'
model_path = utils.make_directory(results_path, 'model_params/mnist')

# dataset path
data_path = '../data/Synthetic_dataset.h5'
train, valid, test = helper.load_synthetic_dataset(data_path)

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

train['inputs'] = np.reshape(mnist.train.images, [-1, 28, 28, 1])
train['targets'] = mnist.train.labels

valid['inputs'] = np.reshape(mnist.validation.images, [-1, 28, 28, 1])
valid['targets'] = mnist.validation.labels

Ejemplo n.º 5

import os, sys
import numpy as np
import helper
import matplotlib.pyplot as plt
import tensorflow as tf
from deepomics import neuralnetwork as nn
from deepomics import utils, visualize

#------------------------------------------------------------------------------------------------


all_models = ['cnn_2', 'cnn_4', 'cnn_10', 'cnn_25', 'cnn_50', 'cnn_100',
			  'cnn_50_2', 'cnn9_4', 'cnn9_25']

# save path
results_path = utils.make_directory('../results', 'invivo')
params_path = utils.make_directory(results_path, 'model_params')
save_path = utils.make_directory(results_path, 'conv_filters')

# get data shapes
input_shape = [None, 1000, 1, 4]
output_shape = [None, 12]

# loop through models
num_params_all = []
roc_scores = []
pr_scores = []
roc_all = []
pr_all = []
for model_name in all_models:
	print('model: ' + model_name)

Ejemplo n.º 6

import os, sys
import numpy as np
import helper
import tensorflow as tf
from deepomics import neuralnetwork as nn
from deepomics import utils, fit

#------------------------------------------------------------------------------------------------

all_models = [
    'cnn_2', 'cnn_4', 'cnn_10', 'cnn_25', 'cnn_50', 'cnn_100', 'cnn_50_2',
    'cnn9_4', 'cnn9_25'
]

# save path
results_path = utils.make_directory('../results', 'invivo')
params_path = utils.make_directory(results_path, 'model_params')

# load dataset
data_path = '../data/invivo_dataset.h5'
train, valid, test = helper.load_invivo_dataset(data_path)

# get data shapes
input_shape = list(train['inputs'].shape)
input_shape[0] = None
output_shape = [None, train['targets'].shape[1]]

# loop through models
for model_name in all_models:
    print('model: ' + model_name)

Ejemplo n.º 7

Archivo: full_fom_analysis.py Proyecto: steffanpaul/uncovering_regulatory_codes

It will also output an array in the shape (num_models, num_reg) showing the name of each model for ease of indexing into the larger model.

'''
#------------------------------------------------------------------------------------------------

all_models = ['DistNet', 'LocalNet', 'DeepBind', 'StandardNet']
num_models = len(all_models)  # number of models
dropout_status = [True, True, False, False, False, True, True, False]
l2_status = [True, False, True, False, False, True, False, True]
bn_status = [True, False, False, True, False, False, True, True]
num_reg = len(dropout_status)  # number of regularization types

# save path
results_path = '../results'
params_path = utils.make_directory(results_path, 'model_params')

# dataset path
data_path = '../data/Synthetic_dataset.h5'

# load dataset
train, valid, test = helper.load_synthetic_dataset(data_path)
#Get the indices of correctly labelled sequences
right_index = np.where(test['targets'][:, 0] == 1)[0]
num_pos = len(right_index)  # number of positively labelled sequences

# get data shapes
input_shape = list(train['inputs'].shape)
input_shape[0] = None

_, seqlen, _, dims = test[

Ejemplo n.º 8

Archivo: 2_print_performance.py Proyecto: robo-sq/robust_features_with_inductive_bias

from deepomics import utils, fit, visualize, saliency, metrics
from sklearn.metrics import roc_curve, auc, precision_recall_curve, accuracy_score, roc_auc_score
import helper
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
np.random.seed(247)
tf.set_random_seed(247)

#---------------------------------------------------------------------------------------------------------

all_models = ['LocalNet']
noise_status = [False, True, False]
adv_status = [False, False, True]

# save path
results_path = '../results'
model_path = utils.make_directory(results_path, 'model_params')
save_path = utils.make_directory(results_path, 'conv_filters')

# dataset path
data_path = '../data/Synthetic_dataset.h5'
train, valid, test = helper.load_synthetic_dataset(data_path)

# get data shapes
input_shape = list(train['inputs'].shape)
input_shape[0] = None
output_shape = [None, train['targets'].shape[1]]

with open(os.path.join(results_path, 'performance.tsv'), 'wb') as f:

    for n, noise in enumerate(noise_status):
        # loop through models

Ejemplo n.º 9

Archivo: 4_2ndlayer_filter_analysis_synthetic.py Proyecto: zhu-j/learning_sequence_motifs

import os, sys
import numpy as np
import helper
import matplotlib.pyplot as plt
import tensorflow as tf
from deepomics import neuralnetwork as nn
from deepomics import utils, visualize


# plot 2nd layer filters of CNN-1
model_name = 'cnn_1'
num_trials = 5

for trial in range(num_trials):

    results_path = utils.make_directory('../results', 'synthetic_'+str(trial))
    params_path = utils.make_directory(results_path, 'model_params')
    save_path = utils.make_directory(results_path, 'conv_filters')

    # load dataset
    data_path = '../data/synthetic_dataset.h5'
    train, valid, test = helper.load_synthetic_dataset(data_path)

    # get data shapes
    input_shape = list(train['inputs'].shape)
    input_shape[0] = None
    output_shape = [None, train['targets'].shape[1]]

    print('model: ' + model_name)
    tf.reset_default_graph()
    tf.set_random_seed(247)

Ejemplo n.º 10

Archivo: main_genome_classification.py Proyecto: peter-koo/Deepomics

train, valid, test = load_data.simulation_pickle(file_path)

#-------------------------------------------------------------------------------------

# build network
shape = (None, train[0].shape[1], train[0].shape[2], train[0].shape[3])
num_labels = train[1].shape[1]
network, placeholders, optimization = standard_model.model(shape, num_labels)

# build neural network class
nnmodel = nn.NeuralNet(network, placeholders)
nnmodel.inspect_layers()

# set output file paths
output_name = 'test'
utils.make_directory(data_path, 'Results')
file_path = os.path.join(data_path, 'Results', output_name)
nntrainer = nn.NeuralTrainer(nnmodel, optimization, save='best', file_path=file_path)

# train model
learn.train_minibatch(nntrainer, data={'train': train, 'valid': valid}, 
                              batch_size=100, num_epochs=500, patience=10, verbose=1)

# load best model --> lowest cross-validation error
nntrainer.set_best_parameters()

# test model
nntrainer.test_model(test, name="test", batch_size=100)

# save all performance metrics (train, valid, test)
nntrainer.save_all_metrics(file_path)