# In[1]: import numpy as np from os.path import expanduser, join import os import json import theano import pyret.filtertools as ft from preprocessing import datagen, loadexpt from utils import rolling_window, mksavedir import h5py from scipy.stats import pearsonr import preprocessing # make save directory save_dir = mksavedir(prefix='Experiment STC') # # Load white noise data # In[2]: whitenoise_train = loadexpt(0, 'whitenoise', 'train', 40, roll=False) # In[5]: import os #f = h5py.File(os.path.join(preprocessing.datadirs['lane.local'], '15-10-07/whitenoise.h5'), 'r') f = h5py.File(os.path.join(preprocessing.datadirs['lenna'], '15-10-07/whitenoise.h5'), 'r')
res_constrained = minimize(model_separation,
x0=initial_guess,
constraints=constraint,
method='COBYLA')
optimal_stimulus = res_constrained.x
constraint_violation = unit_norm_constraint(optimal_stimulus)
temporal_kernel = optimal_stimulus[:40]
spatial_profile = optimal_stimulus[40:]
low_rank_optimal_stimulus = np.outer(temporal_kernel, spatial_profile)
optimal_stimulus = low_rank_optimal_stimulus.reshape((1, 40, 50, 50))
ln_response = ln_model.predict(optimal_stimulus)[0][0]
convnet_response = naturalscenes_model.predict(optimal_stimulus)[0][0]
responses = np.array([ln_response, convnet_response])
## SAVE RESULT ##
save_dir = mksavedir(prefix='Maximal Differentiated Stimuli')
f = h5py.File(join(save_dir, 'differentiated_stimuli.h5'), 'w')
f.create_dataset('stimulus', data=optimal_stimulus)
f.create_dataset('responses', data=responses)
f.create_dataset('constraint', data=constraint_violation)
f.close()
spatial_profile, time = ft.decompose(optimal_stimulus[0])
fig_filename = 'differentiated_stimuli.png'
plt.imshow(spatial_profile, interpolation='nearest')
plt.grid('off')
plt.savefig(join(save_dir, fig_filename))
def __init__(self, cell_index, stimulus_type, loss, optimizer, mean_adapt):
"""
Superclass for managing keras models
Parameters
----------
cell_index : int
stimulus_type : string
Either 'naturalscene' or 'whitenoise'
loss : string or object, optional
The loss function to use. (Default: poisson_loss)
See http://keras.io/objectives/ for more information
optimizer : string or object
The optimizer to use. (Default: sgd)
See http://keras.io/optimizers/ for more information
"""
# compile the model
with notify('Compiling'):
self.model.compile(loss=loss, optimizer=optimizer)
# save architecture as a json file
self.savedir = mksavedir(prefix=str(self))
with notify('Saving architecture'):
with open(join(self.savedir, 'architecture.json'), 'w') as f:
f.write(self.model.to_json())
# function to write data to a CSV file
self.save_csv = partial(tocsv, join(self.savedir, 'performance'))
self.save_csv(['Epoch', 'Iteration', 'Training CC', 'Test CC'])
# load experimental data
self.stimulus_type = stimulus_type
if str(self) == 'lstm':
numTime = self.stim_shape[0]
self.holdout = loadexpt(cell_index, self.stimulus_type, 'test', self.stim_shape[1], mean_adapt=mean_adapt)
self.training = loadexpt(cell_index, self.stimulus_type, 'train', self.stim_shape[1], mean_adapt=mean_adapt)
X_train = self.training.X
y_train = self.training.y
X_test = self.holdout.X
y_test = self.holdout.y
numTrain = (int(X_train.shape[0]/numTime))*numTime
numTest = (int(X_test.shape[0]/numTime))*numTime
X_train = X_train[:numTrain]
y_train = y_train[:numTrain]
X_test = X_test[:numTest]
y_test = y_test[:numTest]
X_train = np.reshape(X_train, (int(numTrain/numTime), numTime, self.stim_shape[1], self.stim_shape[2], self.stim_shape[3]))
y_train = np.reshape(y_train, (int(numTrain/numTime), numTime, 1))
X_test = np.reshape(X_test, (int(numTest/numTime), numTime, self.stim_shape[1], self.stim_shape[2], self.stim_shape[3]))
y_test = np.reshape(y_test, (int(numTest/numTime), numTime, 1))
self.training = Batch(X_train, y_train)
self.holdout = Batch(X_test, y_test)
else:
self.holdout = loadexpt(cell_index, self.stimulus_type, 'test', self.stim_shape[0], mean_adapt=mean_adapt)
self.training = loadexpt(cell_index, self.stimulus_type, 'train', self.stim_shape[0], mean_adapt=mean_adapt)
# save model information to a markdown file
if 'architecture' not in self.__dict__:
self.architecture = 'No architecture information specified'
metadata = ['# ' + str(self), '## ' + strftime('%B %d, %Y'),
'Started training on: ' + strftime('%I:%M:%S %p'),
'### Architecture', self.architecture,
'### Stimulus', 'Experiment 10-07-15', stimulus_type, 'Mean adaptation: ' + str(mean_adapt),
'Cell #{}'.format(cell_index),
'### Optimization', str(loss), str(optimizer)]
tomarkdown(join(self.savedir, 'README'), metadata)
initial_guess = zscore(np.random.randint(0, 2, 40 + 50*50).astype('float32'))
res_constrained = minimize(model_separation, x0=initial_guess, constraints=constraint, method='COBYLA')
optimal_stimulus = res_constrained.x
constraint_violation = unit_norm_constraint(optimal_stimulus)
temporal_kernel = optimal_stimulus[:40]
spatial_profile = optimal_stimulus[40:]
low_rank_optimal_stimulus = np.outer(temporal_kernel, spatial_profile)
optimal_stimulus = low_rank_optimal_stimulus.reshape((1,40,50,50))
ln_response = ln_model.predict(optimal_stimulus)[0][0]
convnet_response = naturalscenes_model.predict(optimal_stimulus)[0][0]
responses = np.array([ln_response, convnet_response])
## SAVE RESULT ##
save_dir = mksavedir(prefix='Maximal Differentiated Stimuli')
f = h5py.File(join(save_dir, 'differentiated_stimuli.h5'), 'w')
f.create_dataset('stimulus', data=optimal_stimulus)
f.create_dataset('responses', data=responses)
f.create_dataset('constraint', data=constraint_violation)
f.close()
spatial_profile, time = ft.decompose(optimal_stimulus[0])
fig_filename = 'differentiated_stimuli.png'
plt.imshow(spatial_profile, interpolation='nearest')
plt.grid('off')
plt.savefig(join(save_dir, fig_filename))
import numpy as np
from os.path import expanduser, join
import os
import json
import theano
import pyret.filtertools as ft
import pyret.visualizations as pyviz
from preprocessing import datagen, loadexpt
from utils import rolling_window
from keras.models import model_from_json
import h5py
import matplotlib.pyplot as plt
from utils import mksavedir
save_dir = mksavedir(prefix='Contrast Steps')
# GET LN AND CNN RESPONSES TO CONTRAST STEPS
architecture_filename = 'architecture.json'
naturalscenes_data_dir = expanduser(
'~/Dropbox/deep-retina/saved/lenna.salamander/2015-11-07 16.52.44 convnet/'
)
naturalscenes_weight_filename = 'epoch038_iter02700_weights.h5' # .53 cc on held-out
ln_data_dir = expanduser(
'~/Dropbox/deep-retina/saved/lenna.nirum/2015-11-08 04.41.18 LN/')
ln_weight_filename = 'epoch010_iter00750_weights.h5' # .468 cc on held-out
# LOAD NATURAL SCENES MODEL
naturalscenes_architecture_data = open(
naturalscenes_data_dir + architecture_filename, 'r')
naturalscenes_architecture_string = naturalscenes_architecture_data.read()
naturalscenes_model = model_from_json(naturalscenes_architecture_string)
def __init__(self, cell_index, stimulus_type, loss, optimizer, mean_adapt):
"""
Superclass for managing keras models
Parameters
----------
cell_index : int
stimulus_type : string
Either 'naturalscene' or 'whitenoise'
loss : string or object, optional
The loss function to use. (Default: poisson_loss)
See http://keras.io/objectives/ for more information
optimizer : string or object
The optimizer to use. (Default: sgd)
See http://keras.io/optimizers/ for more information
"""
# compile the model
with notify('Compiling'):
self.model.compile(loss=loss, optimizer=optimizer)
# save architecture as a json file
self.savedir = mksavedir(prefix=str(self))
with notify('Saving architecture'):
with open(join(self.savedir, 'architecture.json'), 'w') as f:
f.write(self.model.to_json())
# function to write data to a CSV file
self.save_csv = partial(tocsv, join(self.savedir, 'performance'))
self.save_csv(['Epoch', 'Iteration', 'Training CC', 'Test CC'])
# load experimental data
self.stimulus_type = stimulus_type
if str(self) == 'lstm':
numTime = self.stim_shape[0]
self.holdout = loadexpt(cell_index,
self.stimulus_type,
'test',
self.stim_shape[1],
mean_adapt=mean_adapt)
self.training = loadexpt(cell_index,
self.stimulus_type,
'train',
self.stim_shape[1],
mean_adapt=mean_adapt)
X_train = self.training.X
y_train = self.training.y
X_test = self.holdout.X
y_test = self.holdout.y
numTrain = (int(X_train.shape[0] / numTime)) * numTime
numTest = (int(X_test.shape[0] / numTime)) * numTime
X_train = X_train[:numTrain]
y_train = y_train[:numTrain]
X_test = X_test[:numTest]
y_test = y_test[:numTest]
X_train = np.reshape(
X_train, (int(numTrain / numTime), numTime, self.stim_shape[1],
self.stim_shape[2], self.stim_shape[3]))
y_train = np.reshape(y_train,
(int(numTrain / numTime), numTime, 1))
X_test = np.reshape(
X_test, (int(numTest / numTime), numTime, self.stim_shape[1],
self.stim_shape[2], self.stim_shape[3]))
y_test = np.reshape(y_test, (int(numTest / numTime), numTime, 1))
self.training = Batch(X_train, y_train)
self.holdout = Batch(X_test, y_test)
else:
self.holdout = loadexpt(cell_index,
self.stimulus_type,
'test',
self.stim_shape[0],
mean_adapt=mean_adapt)
self.training = loadexpt(cell_index,
self.stimulus_type,
'train',
self.stim_shape[0],
mean_adapt=mean_adapt)
# save model information to a markdown file
if 'architecture' not in self.__dict__:
self.architecture = 'No architecture information specified'
metadata = [
'# ' + str(self), '## ' + strftime('%B %d, %Y'),
'Started training on: ' + strftime('%I:%M:%S %p'),
'### Architecture', self.architecture, '### Stimulus',
'Experiment 10-07-15', stimulus_type,
'Mean adaptation: ' + str(mean_adapt),
'Cell #{}'.format(cell_index), '### Optimization',
str(loss),
str(optimizer)
]
tomarkdown(join(self.savedir, 'README'), metadata)
import numpy as np
from os.path import expanduser, join
import os
import json
import theano
import pyret.filtertools as ft
import pyret.visualizations as pyviz
from preprocessing import datagen, loadexpt
from utils import rolling_window
from keras.models import model_from_json
import h5py
import matplotlib.pyplot as plt
from utils import mksavedir
save_dir = mksavedir(prefix="Contrast Steps")
# GET LN AND CNN RESPONSES TO CONTRAST STEPS
architecture_filename = "architecture.json"
naturalscenes_data_dir = expanduser("~/Dropbox/deep-retina/saved/lenna.salamander/2015-11-07 16.52.44 convnet/")
naturalscenes_weight_filename = "epoch038_iter02700_weights.h5" # .53 cc on held-out
ln_data_dir = expanduser("~/Dropbox/deep-retina/saved/lenna.nirum/2015-11-08 04.41.18 LN/")
ln_weight_filename = "epoch010_iter00750_weights.h5" # .468 cc on held-out
# LOAD NATURAL SCENES MODEL
naturalscenes_architecture_data = open(naturalscenes_data_dir + architecture_filename, "r")
naturalscenes_architecture_string = naturalscenes_architecture_data.read()
naturalscenes_model = model_from_json(naturalscenes_architecture_string)
naturalscenes_model.load_weights(naturalscenes_data_dir + naturalscenes_weight_filename)
# LOAD LN MODEL
ln_architecture_data = open(ln_data_dir + architecture_filename, "r")
