def __init__(self,n_inputs=1):
self.hp = HP()
self.hp.alpha_force = 1.
self.hp.N = 1000
self.hp.g = 1.5
self.hp.nl = np.tanh
self.hp.dt=0.1
self.hp.n_inputs = n_inputs
self.reset()
class ESN:
def __init__(self,n_inputs=1):
self.hp = HP()
self.hp.alpha_force = 1.
self.hp.N = 1000
self.hp.g = 1.5
self.hp.nl = np.tanh
self.hp.dt=0.1
self.hp.n_inputs = n_inputs
self.reset()
def reset(self):
self.W=self.hp.g*np.random.normal(size=[self.hp.N,self.hp.N])/np.sqrt(self.hp.N)
self.wfb=np.random.normal(size=[self.hp.N,1])
self.win=np.random.normal(size=[self.hp.N,self.hp.n_inputs])
self.wout=np.zeros([self.hp.N,1])
self.x = np.random.normal(size=[self.hp.N,1])
self.r = self.hp.nl(self.x)
self.FORCE_reset()
def step(self,f=None, uin=None):
self.r = self.hp.nl(self.x)
self.z =np.matmul(self.wout.transpose(),self.r)
self.x *= 1-self.hp.dt
self.x += self.hp.dt*(np.matmul(self.W,self.r) + self.wfb*(self.z if f is None else f) + (np.matmul(self.win,uin.reshape([-1,1])) if uin is not None else 0))
def train_batch():
error
def train_FORCE(self, f_vec=None, uin_vec=None,tmax=None):
rec = []
self.FORCE_reset()
for ti, (u,f) in enumerate(zip(uin_vec,f_vec)):
self.step(uin=u)
self.FORCE_step(f)
rec.append(self.z)
return rec
def FORCE_reset(self):
self.P = self.hp.alpha_force*np.eye(self.hp.N,self.hp.N)
def FORCE_step(self,f):
k = np.matmul(self.P,self.r)
rPr = np.matmul(self.r.transpose(),k)
c = 1.0/(1.0 + rPr)
self.P -= np.matmul(k,np.transpose(k*c))
e = self.z-f
dw = -e*k*c
self.wout += dw
# scale pixels
def prep_pixels(train, test):
# convert from integers to floats
train_norm = train.astype('float32')
test_norm = test.astype('float32')
# normalize to range 0-1
train_norm = train_norm / 255.0
test_norm = test_norm / 255.0
# return normalized images
return train_norm, test_norm
# prepare pixel data
trainX, testX = prep_pixels(trainX, testX)
hp = HP()
hp.save_path = 'saved_runs'
hp.description = "syclop micro feature learning runs"
hp.this_run_name = 'micro_{}'.format(run_index)
deploy_logs()
#%%
############################### Get Trained Teacher ##########################3
path = '/home/orram/Documents/GitHub/imagewalker/teacher_student/'
path = '/home/labs/ahissarlab/orra/imagewalker/teacher_student/'
def train_model(path, trainX, trainY):
def net():
input = keras.layers.Input(shape=(32, 32, 3))
def split_dataset_xy(dataset):
dataset_x1 = [uu[0] for uu in dataset]
dataset_x2 = [uu[1] for uu in dataset]
dataset_y = [uu[-1] for uu in dataset]
return (np.array(dataset_x1)[..., np.newaxis],
np.array(dataset_x2)[:, :n_timesteps, :]), np.array(dataset_y)
#parse hyperparameters
lsbjob = os.getenv('LSB_JOBID')
lsbjob = '' if lsbjob is None else lsbjob
hp = HP()
hp.save_path = 'saved_runs'
hp.description = ''
parser = argparse.ArgumentParser()
parser.add_argument('--tau_int',
default=4.,
type=float,
help='Integration timescale for adaaptation')
parser.add_argument('--resize',
default=1.0,
type=float,
help='resize of images')
parser.add_argument('--run_name_suffix',
default='',
type=str,
help='suffix for runname')