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train.py
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train.py
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"""Collection of functions to train the hierarchical model."""
from __future__ import print_function
import numpy as np
import models
import batch_utils
import data_transforms
import McNeuron
from keras.optimizers import RMSprop
import matplotlib.pyplot as plt
def clip_weights(model, weight_constraint):
"""
Clip weights of a keras model to be bounded by given constraints.
Parameters
----------
model: keras model object
model for which weights need to be clipped
weight_constraint:
Returns
-------
model: keras model object
model with clipped weights
"""
for l in model.layers:
weights = l.get_weights()
weights = \
[np.clip(w, weight_constraint[0],
weight_constraint[1]) for w in weights]
l.set_weights(weights)
return model
def save_model_weights():
"""
cool stuff.
"""
def plot_example_neuron(X_locations, X_prufer):
"""
Show an example neuron.
stuff.
"""
locations = np.squeeze(X_locations)
prufer = np.squeeze(X_prufer).argmax(axis=1)
soma = np.array([[0., 0., 0.]])
np.append(soma, np.squeeze(locations), axis=0)
parents = np.array(data_transforms.decode_prufer(list(prufer)))
parents_reordered, locations_reordered = \
data_transforms.reordering_prufer(parents, np.squeeze(locations))
prufer_reordered = data_transforms.encode_prufer(list(parents_reordered))
input_code = dict()
input_code['morphology'] = np.array(prufer_reordered)
input_code['geometry'] = np.squeeze(locations_reordered)
neuron_object = \
data_transforms.make_swc_from_prufer_and_locations(input_code)
McNeuron.visualize.plot_2D(neuron_object)
return neuron_object
def train_model(training_data=None,
n_levels=3,
n_nodes=[10, 20, 40],
input_dim=100,
n_epochs=25,
batch_size=64,
n_batch_per_epoch=100,
d_iters=20,
lr_discriminator=0.005,
lr_generator=0.00005,
weight_constraint=[-0.01, 0.01],
verbose=True):
"""
Train the hierarchical model.
Progressively generate trees with
more and more nodes.
Parameters
----------
training_data: dict of dicts
each inner dict is an array
'geometry': 3-d arrays (locations)
n_samples x n_nodes - 1 x 3
'morphology': 2-d arrays
n_samples x n_nodes - 2 (prufer sequences)
example: training_data['geometry']['n20'][0:10, :, :]
gives the geometry for the first 10 neurons
training_data['geometry']['n20'][0:10, :]
gives the prufer sequences for the first 10 neurons
here, 'n20' indexes a key corresponding to
20-node downsampled neurons.
n_levels: int
number of levels in the hierarchy
n_nodes: list of length n_levels
specifies the number of nodes for each level.
should be consistent with training data.
input_dim: int
dimensionality of noise input
n_epochs:
number of epochs over training data
batch_size:
batch size
n_batch_per_epoch: int
number of batches per epoch
d_iters: int
number of iterations to train discriminator
lr_discriminator: float
learning rate for optimization of discriminator
lr_generator: float
learning rate for optimization of generator
weight_constraint: array
upper and lower bounds of weights (to clip)
verbose: bool
print relevant progress throughout training
Returns
-------
geom_model: list of keras model objects
geometry generators for each level
morph_model: list of keras model objects
morphology generators for each level
disc_model: list of keras model objects
discriminators for each level
gan_model: list of keras model objects
discriminators stacked on generators for each level
"""
# ###################################
# Initialize models at all levels
# ###################################
geom_model = list()
morph_model = list()
disc_model = list()
gan_model = list()
for level in range(n_levels):
# Discriminator
d_model = models.discriminator(n_nodes_in=n_nodes[level])
# Generators and GANs
# If we are in the first level, no context
if level == 0:
g_model, m_model = \
models.generator(use_context=False,
n_nodes_out=n_nodes[level])
gd_model = \
models.discriminator_on_generators(g_model,
m_model,
d_model,
input_dim=input_dim,
use_context=False)
# In subsequent levels, we need context
else:
g_model, m_model = \
models.generator(use_context=True,
n_nodes_in=n_nodes[level-1],
n_nodes_out=n_nodes[level])
gd_model = \
models.discriminator_on_generators(g_model,
m_model,
d_model,
input_dim=input_dim,
n_nodes_in=n_nodes[level-1],
use_context=True)
# Collect all models into a list
disc_model.append(d_model)
geom_model.append(g_model)
morph_model.append(m_model)
gan_model.append(gd_model)
# ###############
# Optimizers
# ###############
optim_d = RMSprop(lr=lr_discriminator)
optim_g = RMSprop(lr=lr_generator)
# ##############
# Train
# ##############
for level in range(n_levels):
# ---------------
# Compile models
# ---------------
g_model = geom_model[level]
m_model = morph_model[level]
d_model = disc_model[level]
gd_model = gan_model[level]
g_model.compile(loss='mse', optimizer=optim_g)
m_model.compile(loss='mse', optimizer=optim_g)
d_model.trainable = False
gd_model.compile(loss=models.wasserstein_loss, optimizer=optim_g)
d_model.trainable = True
d_model.compile(loss=models.wasserstein_loss, optimizer=optim_d)
if verbose:
print("")
print(20*"=")
print("Level #{0}".format(level))
print(20*"=")
# -----------------
# Loop over epochs
# -----------------
for e in range(n_epochs):
batch_counter = 1
g_iters = 0
if verbose:
print("")
print(" Epoch #{0}".format(e))
print("")
while batch_counter < n_batch_per_epoch:
list_d_loss = list()
list_g_loss = list()
# ----------------------------
# Step 1: Train discriminator
# ----------------------------
for d_iter in range(d_iters):
# Clip discriminator weights
d_model = clip_weights(d_model, weight_constraint)
# Create a batch to feed the discriminator model
X_locations_real, X_prufer_real = \
batch_utils.get_batch(training_data=training_data,
batch_size=batch_size,
batch_counter=batch_counter,
n_nodes=n_nodes[level])
y_real = -np.ones((X_locations_real.shape[0], 1, 1))
#print X_locations_real.shape, X_prufer_real.shape, y_real.shape
X_locations_gen, X_prufer_gen = \
batch_utils.gen_batch(batch_size=batch_size,
n_nodes=n_nodes,
level=level,
input_dim=input_dim,
geom_model=geom_model,
morph_model=morph_model)
y_gen = np.ones((X_locations_gen.shape[0], 1, 1))
#print X_locations_gen.shape, X_prufer_gen.shape, y_gen.shape
X_locations = np.concatenate((X_locations_real,
X_locations_gen), axis=0)
X_prufer = np.concatenate((X_prufer_real,
X_prufer_gen), axis=0)
y = np.concatenate((y_real, y_gen), axis=0)
# Update the discriminator
#d_model.summary()
disc_loss = \
d_model.train_on_batch([X_locations,
X_prufer],
y)
list_d_loss.append(disc_loss)
if verbose:
print(" After {0} iterations".format(d_iters))
print(" Discriminator Loss \
= {0}".format(disc_loss))
# ------------------------
# Step 2: Train generator
# ------------------------
# Freeze the discriminator
d_model.trainable = False
if level > 0:
X_locations_prior_gen, X_prufer_prior_gen = \
batch_utils.gen_batch(batch_size=batch_size,
n_nodes=n_nodes,
level=level-1,
input_dim=input_dim,
geom_model=geom_model,
morph_model=morph_model)
noise_input = np.random.randn(batch_size, 1, input_dim)
if level == 0:
gen_loss = \
gd_model.train_on_batch([noise_input],
y_real)
else:
gen_loss = \
gd_model.train_on_batch([X_locations_prior_gen,
X_prufer_prior_gen,
noise_input],
y_real)
list_g_loss.append(gen_loss)
if verbose:
print("")
print(" Generator_Loss: {0}".format(gen_loss))
# Unfreeze the discriminator
d_model.trainable = True
# ---------------------
# Step 3: Housekeeping
# ---------------------
g_iters += 1
batch_counter += 1
# Save model weights (few times per epoch)
print(batch_counter)
if batch_counter % 25 == 0:
#save_model_weights(g_model,
# m_model,
# level,
# epoch,
# batch_counter)
if verbose:
print (" Level #{0} Epoch #{1} Batch #{2}".
format(level, e, batch_counter))
neuron_object = \
plot_example_neuron(X_locations_gen[0, :, :],
X_prufer_gen[0, :, :])
plt.show()
# Display loss trace
if 0:
plt.figure(figsize=(3, 2))
plt.plot(list_d_loss)
plt.show()
# Save models
geom_model[level] = g_model
morph_model[level] = m_model
disc_model[level] = d_model
gan_model[level] = gd_model
return geom_model, morph_model, disc_model, gan_model