def Load_pickle():
pickle = Pickle()
label = pickle.load_label_pickle("label_pickle")
feature = pickle.load_feature_pickle("feature_pickle")
return feature,label
epochs = 1000
batch_size = 40
model.fit(feature,
label,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2,
callbacks=callbacks)
if __name__ == "__main__":
pickle = Pickle()
## loading dataset
label = pickle.load_label_pickle("label_pickle")
feature = pickle.load_feature_pickle("feature_pickle")
# converting numbers btw 0-1 and converting format to float50
for count in range(len(feature)):
feature[count] = feature[count] / 255.0
feature[count] = feature[count].astype("float32")
## convertin feature and label to numpy array
feature = numpy.array(feature).reshape(-1, 48, 48, 1)
label = numpy.array(label)
cnn_model(feature, label)