return loss_func
gr_img = plt.imread('data/car.png')[:, :, :3]
M, N, C = gr_img.shape
sigma = 0.1
np.random.seed(10)
noisy_img = gr_img + sigma * np.random.randn(M, N, C)
noisy_img = np.vectorize(lambda x: 1.0 if x > 1 else 0.0
if x < 0 else x)(noisy_img)
F = 16
model = get_Model(Input((M, N, F)), n_filters=8, depth=4, output_channels=C)
model.compile(optimizer=Adam(), loss='mse', metrics=[mse_gr(gr_img)])
np.random.seed(2)
z = np.random.uniform(low=-1, high=1, size=(1, M, N, F))
model.fit(z, noisy_img.reshape((1, M, N, C)), epochs=2000, verbose=1)
j = model.predict(z)
plt.figure()
plt.subplot(3, 1, 1)
plt.imshow(noisy_img)
plt.subplot(3, 1, 2)
#%%
from PIL import Image
import numpy as np
import pickle
import model
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
import itertools
import keras.backend as K
#%%
miniModel = model.get_Model(training=False)
miniModel.load_weights('models\\6-11[17-54]__Model\\bestModel.h5')
#%%
with open('alphabetList.txt', 'rb') as file:
alphabetList = pickle.load(file)
#%%
def single_test(imagePath):
image = Image.open(imagePath)
image = image.convert(mode='L')
testArray = np.asarray(image.transpose(Image.FLIP_LEFT_RIGHT))
SC = MinMaxScaler()
testScaled = SC.fit_transform(testArray)
testScaled = testScaled.T
testScaled = np.expand_dims(testScaled, axis=-1)
testScaled = np.expand_dims(testScaled, axis=0)
return testScaled
#%%
from keras import backend as K
from keras.optimizers import Adadelta, Adam
from keras.callbacks import EarlyStopping, ModelCheckpoint
import data_generator
import model
import parameters as p
import numpy as np
import os
import jdatetime
K.set_learning_phase(0)
model = model.get_Model(training=True)
EPOCHS = 150
# load weight from previous trainings
LOADING = 0
WEIGHT_DIR = 'models\\6-2[18-22]__Model\\bestModel.h5'
if LOADING == 1:
model.load_weights(WEIGHT_DIR)
# Model description and training
now = jdatetime.datetime.today()
model_folder_name = '%s-%s[%s-%s]__Model' % (now.month, now.day, now.hour,
now.minute)
os.mkdir('models\\%s' % model_folder_name)
info = input('enter train note: ')
var = K.mean(K.square(y_pred[:, 1:, :] - y_pred[:, :-1, :])) + K.mean(
K.square(y_pred[:, :, 1:] - y_pred[:, :, :-1]))
return mse + var / 12
return loss_func
img = np.zeros((56, 48))
img[2:-2, 2:-2] = plt.imread('data/mri_lowres.png')
M, N = img.shape
F = 16
model = get_Model(Input((M, N, F)),
n_filters=16,
depth=3,
res_exp=2,
output_channels=1)
model.compile(optimizer=Adam(), loss=scaled_loss(2))
np.random.seed(2)
z = np.random.uniform(low=-1, high=1, size=(1, M, N, F))
bmcb = BMCallback()
model.fit(z,
img.reshape((1, M, N, 1)),
epochs=3000,
verbose=1,
callbacks=[bmcb])
model.set_weights(bmcb.best_weights)