from CNN import CNN
from LoadData import LoadData
load_train = LoadData()
data_train = load_train.data_train
train_generator, x_train, x_valid, y_train, y_valid = load_train.loadDataTrain(
)
input_shape = (load_train.img_rows, load_train.img_cols, 1)
epochs = 100
lr = 0.0002
batch_size = 32
cnn = CNN(input_shape, len(train_generator.class_indices), lr)
model = cnn.ConvNetModel()
# history = model.fit(x_train, y_train,
# batch_size=batch_size,
# epochs=epochs,
# verbose=1,
# validation_data=(x_valid, y_valid))
model.fit(x_train,
y_valid,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_valid, y_valid))
evaluation = model.evaluate(x_valid, y_valid, batch_size=batch_size, verbose=1)
print('loss : %.2f, accuracy : %.2f' % (evaluation[0], evaluation[1]))
