def load_Data():
naturalscenes_test = loadexpt(cell_index, "naturalscene", "test", 40)
X_test = naturalscenes_test.X
y_test = naturalscenes_test.y
numTest = (int(X_test.shape[0] / numTime)) * numTime
X_test = X_test[:numTest]
y_test = y_test[:numTest]
X_test = np.reshape(X_test, (int(numTest / numTime), numTime, 40, 50, 50))
y_test = np.reshape(y_test, (int(numTest / numTime), numTime, 1))
return X_test, y_test
def load_Data():
naturalscenes_test = loadexpt(cell_index, 'naturalscene', 'test', 40)
X_test = naturalscenes_test.X
y_test = naturalscenes_test.y
numTest = (int(X_test.shape[0]/numTime))*numTime
X_test = X_test[:numTest]
y_test = y_test[:numTest]
X_test = np.reshape(X_test, (int(numTest/numTime), numTime, 40, 50, 50))
y_test = np.reshape(y_test, (int(numTest/numTime), numTime, 1))
return X_test, y_test
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') # In[6]: spk = f['spikes/cell01'] # In[8]:
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)
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)
