num_classes = 4
input_size = 864
model = CNN(input_size=input_size, num_classes=num_classes, optimizer='Adam')
model.load(filepath=model_file)
np.random.seed(0)
global shuffled_idx
shuffled_idx = np.arange(20000)
np.random.shuffle(shuffled_idx)
x_test = []
y_test = []
mini_batch_idx = [shuffled_idx[k] for k in range(1200, 1400)]
print(mini_batch_idx)
n = 0
for i in mini_batch_idx:
j = 0
with open('linechart_csv_15001_20000.csv', 'r') as c:
cr = csv.reader(c)
for line in cr:
if (line[5] == "Legendbbox" and (j == i)):
img = np.array(mpimg.imread(str(line[0])))
img = img[:, :, 0:-1]
x_test.append(img)
y_test.append(list(map(float, line[1:5])))
print('loading image ' + str(n))
j += 1
n += 1
elif (line[5] == "Legendbbox"):
j += 1
print(model.gradients(np.array(x_test), np.array(y_test)))
