import os
import datetime
import numpy as np
batch_size = 256
n_classes = 3
n_epoch = 25
model = the_model(n_channels=2, n_features=3, reg=1e-4, drop=0.5)
dataset = "nuclei_all_61x61"
direc_save = "/home/nquach/DeepCell2/trained_networks/"
direc_data = "/home/nquach/DeepCell2/training_data_npz/"
optimizer = RMSprop(lr=0.001, rho=0.95, epsilon=1e-8)
lr_sched = rate_scheduler(lr=0.001, decay=0.95)
expt = "feature_net_61x61_drop_reg4"
iterate = 4
train_model_sample(model=model,
dataset=dataset,
optimizer=optimizer,
expt=expt,
it=iterate,
batch_size=batch_size,
n_epoch=n_epoch,
direc_save=direc_save,
direc_data=direc_data,
lr_sched=lr_sched,
rotate=True,
flip=True,
shear=0)
import os
import datetime
import numpy as np
batch_size = 128
n_classes = 3
n_epoch = 50
model = the_model(n_channels=2, n_features=3, reg=1e-4)
dataset = "HeLa_set1_81x81"
direc_save = "/home/nquach/DeepCell2/trained_networks/"
direc_data = "/home/nquach/DeepCell2/training_data_npz/"
optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
lr_sched = rate_scheduler(lr=0.01, decay=0.95)
expt = "bn81x81_higher_reg"
iterate = 0
train_model_sample(model=model,
dataset=dataset,
optimizer=optimizer,
expt=expt,
it=iterate,
batch_size=batch_size,
n_epoch=n_epoch,
direc_save="/home/nquach/DeepCell2/trained_networks/",
direc_data="/home/nquach/DeepCell2/training_data_npz/",
lr_sched=lr_sched,
rotate=True,
flip=True,
shear=0)
