def test_2Dexperiment():
c = Config()
c.batch_size = 200
c.n_epochs = 40
c.learning_rate = 0.001
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 200
# model-specific
c.n_coupling = 8
c.prior = 'gauss'
exp = SmileyExperiment(
c,
name='gauss',
n_epochs=c.n_epochs,
seed=42,
base_dir='experiment_dir',
loggers={'visdom': ['visdom', {
"exp_name": "myenv"
}]})
exp.run()
# sampling
samples = exp.model.sample(1000).cpu().numpy()
sns.jointplot(samples[:, 0], samples[:, 1])
plt.show()
def get_config():
c = Config()
# cli flags using trixi, overwrite using e.g. --learning_rate=0.001
c.txt_file = 'assets/001ssb.txt' # Path to a .txt file to train on
c.seq_length = 30 # Length of an input sequence
c.gen_length = 250 # Length of the generated sequence
c.lstm_num_hidden = 128 # Number of hidden units in the LSTM
c.lstm_num_layers = 2 # Number of LSTM layers in the model
# Training params
c.batch_size = 64 # Number of examples to process in a batch
c.learning_rate = 2e-3 # Learning rate
# It is not necessary to implement the following three params, but it may help training.
c.learning_rate_decay = 0.96 # Learning rate decay fraction
c.learning_rate_step = 5000 # Learning rate step
c.dropout_keep_prob = 1.0 # Dropout keep probability
c.train_steps = 1e6 # Number of training steps
c.max_norm = 5.0
# Misc params
c.summary_path = './summaries/' # Output path for summaries
c.print_every = 5 # How often to print training progress
c.sample_every = 100 # How often to sample from the model
c.device = 'cuda:0' # Training device 'cpu' or 'cuda:0'
c.temperature = 0.5 # balances the sampling strategy between fully-greedy (near 0) and fully-random (higher). e.g. 0.5, 1.0, 2.0.
return c
def get_config():
c = Config()
# cli flags using trixi, overwrite using e.g. --learning_rate=0.001
c.model_type = 'RNN' # Model type, should be 'RNN' or 'LSTM'
c.input_length = 10 # Length of an input sequence
c.input_dim = 1 # Dimensionality of input sequence
c.num_classes = 10 # Dimensionality of output sequence
c.num_hidden = 128 # Number of hidden units in the model
c.batch_size = 128 # Number of examples to process in a batch
c.learning_rate = 0.001 # Learning rate
c.train_steps = 10000 # Number of training steps
c.max_norm = 10.0
c.device = 'cuda:0' # Training device 'cpu' or 'cuda:0'
return c
def test_MNIST_experiment():
c = Config()
c.batch_size = 64
c.n_epochs = 50
c.learning_rate = 0.001
c.weight_decay = 5e-5
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 100
c.subset_size = 10
# model-specific
c.n_coupling = 8
c.n_filters = 64
exp = MNISTExperiment(
c,
name='mnist_test',
n_epochs=c.n_epochs,
seed=42,
base_dir='experiment_dir',
loggers={'visdom': ['visdom', {
"exp_name": "myenv"
}]})
exp.run()
exp.model.eval()
exp.model.to('cpu')
with torch.no_grad():
samples = exp.model.sample(16, device='cpu')
img_grid = make_grid(samples).permute((1, 2, 0))
plt.imshow(img_grid)
plt.show()
return exp.model
def test_Resnet():
c = Config()
c.batch_size = 64
c.batch_size_test = 1000
c.n_epochs = 10
c.learning_rate = 0.01
c.momentum = 0.9
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 200
exp = MNIST_classification(config=c,
name='experiment',
n_epochs=c.n_epochs,
seed=42,
base_dir='./experiment_dir',
loggers={"visdom": "visdom"})
exp.run()
def get_config():
c = Config()
c.batch_size = 6
c.patch_size = 512
c.n_epochs = 20
c.learning_rate = 0.0002
c.do_ce_weighting = True
c.do_batchnorm = True
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 200
c.base_dir='/media/kleina/Data2/output/meddec'
c.data_dir='/media/kleina/Data2/Data/meddec/Task07_Pancreas_expert_preprocessed'
c.split_dir='/media/kleina/Data2/Data/meddec/Task07_Pancreas_preprocessed'
c.data_file = 'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/image_gt_data_file_list_all_640x640.csv'
c.additional_slices=5
c.name=''
print(c)
return c
import numpy as np
import torch
from trixi.util import Config
from experiment import MNISTexperiment
from util import plot_dependency_map
import matplotlib.pyplot as plt
c = Config()
c.batch_size = 128
c.n_epochs = 10
c.learning_rate = 0.001
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 100
exp = MNISTexperiment(config=c,
name='test',
n_epochs=c.n_epochs,
seed=c.rnd_seed,
base_dir='./experiment_dir',
loggers={"visdom": ["visdom", {
"exp_name": "myenv"
}]})
# # run backpropagation for each dimension to compute what other
# # dimensions it depends on.
# exp.setup()
# d = 28