from deoxys.model import load_model, Model
import numpy as np
import matplotlib.pyplot as plt
import h5py
deo = load_model(
# '../../oxford_perf/logs_db/5e2e2349a356a4893813c8f7/model/model.030.h5')
# '../../oxford_perf/log_db_ex2/5e3e87d2b26396ccbca9c3e7/model/model.030.h5')
# '../../hn_perf/logs_saved_9epochs/model/model.006.h5')
'../../hn_perf/exps/model.014.h5')
# '../../mnist/logs/model/model.012.h5')
if __name__ == '__main__':
deo.model.summary()
dr = deo.data_reader
imgs = []
targets = []
datagen = dr.val_generator.generate()
# datagen.__next__()
# datagen.__next__()
# x, y = datagen.__next__()
indexes = [10, 205, 230, 390, 834]
k = 5
for i, (x, y) in enumerate(datagen):
for index in indexes:
if i == index // 4:
from deoxys.keras import backend as K
from deoxys.model import load_model, Model
import numpy as np
import matplotlib.pyplot as plt
import h5py
from PIL import Image
if __name__ == '__main__':
# load model
deo = load_model('../../hn_perf/exps/model.014.h5')
# get data reader
dr = deo.data_reader
# get input images, targets, predictions
imgs = []
targets = []
datagen = dr.val_generator.generate()
indexes = [10, 205, 230, 390, 834]
k = len(indexes)
for i, (x, y) in enumerate(datagen):
for index in indexes:
if i == index // 4:
imgs.append(x[index % 4])
targets.append(y[index % 4])
if len(imgs) == k:
break
from deoxys.model import model_from_full_config, load_model
import matplotlib.pyplot as plt
if __name__ == '__main__':
# model = model_from_full_config('config/2d_unet_CT_W_PET.json')
model = load_model('../../hn_perf/2d_unet/model/model.014.h5')
# # data handling here
# model.predict(data)
model.model.summary()
res = model.activation_maximization('conv2d_1')
plt.imshow(res[0][..., 0], )
plt.show()