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hyperparameters.py
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hyperparameters.py
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"""Parameters used for the experiments of the paper."""
import tensorflow as tf
from deepsphere import utils
def get_params(ntrain, EXP_NAME, order, Nside, architecture="FCN", verbose=True):
"""Parameters for the cgcnn and cnn2d defined in deepsphere/models.py"""
n_classes = 2
params = dict()
params['dir_name'] = EXP_NAME
# Types of layers.
params['conv'] = 'chebyshev5' # Graph convolution: chebyshev5 or monomials.
params['pool'] = 'max' # Pooling: max or average.
params['activation'] = 'relu' # Non-linearity: relu, elu, leaky_relu, softmax, tanh, etc.
params['statistics'] = 'mean' # Statistics (for invariance): None, mean, var, meanvar, hist.
# Architecture.
params['F'] = [16, 32, 64, 64, 64, n_classes] # Graph convolutional layers: number of feature maps.
params['K'] = [5] * 6 # Polynomial orders.
params['batch_norm'] = [True] * 6 # Batch normalization.
params['M'] = [] # Fully connected layers: output dimensionalities.
# Pooling.
nsides = [Nside, Nside//2, Nside//4, Nside//8, Nside//16, Nside//32, Nside//32]
params['nsides'] = nsides
params['indexes'] = utils.nside2indexes(nsides, order)
# params['batch_norm_full'] = []
if architecture == "CNN":
# Classical convolutional neural network.
# Replace the last graph convolution and global average pooling by a fully connected layer.
# That is, change the classifier while keeping the feature extractor.
params['F'] = params['F'][:-1]
params['K'] = params['K'][:-1]
params['batch_norm'] = params['batch_norm'][:-1]
params['nsides'] = params['nsides'][:-1]
params['indexes'] = params['indexes'][:-1]
params['statistics'] = None
params['M'] = [n_classes]
elif architecture == "FCN":
# Fully convolutional neural network.
pass
elif architecture == 'FNN':
# Fully connected neural network.
raise NotImplementedError('This is not working!')
params['F'] = []
params['K'] = []
params['batch_norm'] = []
params['indexes'] = []
params['statistics'] = None
params['M'] = [128*order*order, 1024*order, 1024*order, n_classes]
params['batch_norm_full'] = [True]*3
params['input_shape'] = (Nside//order)**2
elif architecture == 'CNN-2d-big':
params['F'] = params['F'][:-1]
params['K'] = [[5, 5]] * 5
params['p'] = [2, 2, 2, 2, 2]
params['input_shape'] = [1024//order, 1024//order]
params['batch_norm'] = params['batch_norm'][:-1]
params['statistics'] = None
params['M'] = [n_classes]
del params['indexes']
del params['nsides']
del params['conv']
elif architecture == 'FCN-2d-big':
params['K'] = [[5, 5]] * 6
params['p'] = [2, 2, 2, 2, 2, 1]
params['input_shape'] = [1024//order, 1024//order]
del params['indexes']
del params['nsides']
del params['conv']
elif architecture == 'CNN-2d':
params['F'] = [8, 16, 32, 32, 16]
params['K'] = [[5, 5]] * 5
params['p'] = [2, 2, 2, 2, 2]
params['input_shape'] = [1024//order, 1024//order]
params['batch_norm'] = params['batch_norm'][:-1]
params['statistics'] = None
params['M'] = [n_classes]
del params['indexes']
del params['nsides']
del params['conv']
elif architecture == 'FCN-2d':
params['F'] = [8, 16, 32, 32, 16, 2]
params['K'] = [[5, 5]] * 6
params['p'] = [2, 2, 2, 2, 2, 1]
params['input_shape'] = [1024//order, 1024//order]
del params['indexes']
del params['nsides']
del params['conv']
else:
raise ValueError('Unknown architecture {}.'.format(architecture))
# Regularization (to prevent over-fitting).
params['regularization'] = 0 # Amount of L2 regularization over the weights (will be divided by the number of weights).
if '2d' in architecture:
params['regularization'] = 3
# elif architecture == 'FNN':
# print('Use regularization new')
# params['regularization'] = 10 # Amount of L2 regularization over the weights (will be divided by the number of weights).
# params['dropout'] = 1 # Percentage of neurons to keep.
params['dropout'] = 1 # Percentage of neurons to keep.
# Training.
params['num_epochs'] = 80 # Number of passes through the training data.
params['batch_size'] = 16 * order**2 # Constant quantity of information (#pixels) per step (invariant to sample size).
# Optimization: learning rate schedule and optimizer.
params['scheduler'] = lambda step: tf.train.exponential_decay(2e-4, step, decay_steps=1, decay_rate=0.999)
params['optimizer'] = lambda lr: tf.train.AdamOptimizer(lr, beta1=0.9, beta2=0.999, epsilon=1e-8)
# Number of model evaluations during training (influence training time).
n_evaluations = 200
params['eval_frequency'] = int(params['num_epochs'] * ntrain / params['batch_size'] / n_evaluations)
if verbose:
print('#sides: {}'.format(nsides))
print('#pixels: {}'.format([(nside//order)**2 for nside in nsides]))
# Number of pixels on the full sphere: 12 * nsides**2.
print('#samples per batch: {}'.format(params['batch_size']))
print('=> #pixels per batch (input): {:,}'.format(params['batch_size']*(Nside//order)**2))
print('=> #pixels for training (input): {:,}'.format(params['num_epochs']*ntrain*(Nside//order)**2))
n_steps = params['num_epochs'] * ntrain // params['batch_size']
lr = [params['scheduler'](step).eval(session=tf.Session()) for step in [0, n_steps]]
print('Learning rate will start at {:.1e} and finish at {:.1e}.'.format(*lr))
return params
def get_params_CNN2D(ntrain, EXP_NAME, order, Nside, architecture='FCN', verbose=True):
"""Parameters for the Healpix2CNN defined in experimental/cnn.py"""
bn = True
params = dict()
params['net'] = dict()
if architecture == "CNN":
params['net']['full'] = [2]
params['net']['nfilter'] = [8, 16, 32, 32, 16]
params['net']['batch_norm'] = [bn, bn, bn, bn, bn]
params['net']['shape'] = [[5, 5], [5, 5], [5, 5], [5, 5], [5, 5]]
params['net']['stride'] = [2, 2, 2, 2, 2]
params['net']['statistics'] = None # 'mean', 'var', 'meanvar'
elif architecture == "FCN":
params['net']['full'] = []
params['net']['nfilter'] = [8, 16, 32, 32, 16, 2]
params['net']['batch_norm'] = [bn, bn, bn, bn, bn, bn]
params['net']['shape'] = [[5, 5], [5, 5], [5, 5], [5, 5], [5, 5], [5, 5]]
params['net']['stride'] = [2, 2, 2, 2, 2, 1]
params['net']['statistics'] = 'mean' # 'mean', 'var', 'meanvar'
elif architecture == "CNN-big":
params['net']['full'] = [2]
params['net']['nfilter'] = [16, 32, 64, 64, 64]
params['net']['batch_norm'] = [bn, bn, bn, bn, bn]
params['net']['shape'] = [[5, 5], [5, 5], [5, 5], [5, 5], [5, 5]]
params['net']['stride'] = [2, 2, 2, 2, 2]
params['net']['statistics'] = None # 'mean', 'var', 'meanvar'
elif architecture == "FCN-big":
params['net']['full'] = []
params['net']['nfilter'] = [16, 32, 64, 64, 64, 2]
params['net']['batch_norm'] = [bn, bn, bn, bn, bn, bn]
params['net']['shape'] = [[5, 5], [5, 5], [5, 5], [5, 5], [5, 5], [5, 5]]
params['net']['stride'] = [2, 2, 2, 2, 2, 1]
params['net']['statistics'] = 'mean' # 'mean', 'var', 'meanvar'
else:
raise ValueError('Unknown architecture {}.'.format(architecture))
params['net']['summary'] = True
params['net']['in_shape'] = [1024//order, 1024//order] # Shape of the image
params['net']['out_shape'] = [2] # Shape of the output (number of class)
params['net']['l2_reg'] = 0 # l2 regularization
# Training.
params['optimization'] = dict()
params['optimization']['epoch'] = 80 # Number of passes through the training data.
params['optimization']['batch_size'] = 16 * order**2 # Constant quantity of information (#pixels) per step (invariant to sample size).
params['optimization']['learning_rate'] = 1e-3
n_evaluations = 200
params['summary_every'] = int(params['optimization']['epoch'] * ntrain / params['optimization']['batch_size'] / n_evaluations)
params['save_dir'] = 'checkpoints/{}/'.format(EXP_NAME)
params['summary_dir'] = 'summaries/{}'.format(EXP_NAME)
params['print_every'] = 10
return params