Splitting the data

Do not forget to split your data into training and test sets before training the model as shown in the following:

train_set = data[(data.Usage == 'Training')] 
test_set = data[(data.Usage != 'Training')] 
X_train = np.array(map(str.split, train_set.pixels), np.float32) 
X_test = np.array(map(str.split, test_set.pixels), np.float32) 
(X_train.shape, X_test.shape) 
((28273, 2304), (7067, 2304)) 
48*48 
2304 
X_train = X_train.reshape(28273, 48, 48, 1) 
X_test = X_test.reshape(7067, 48, 48, 1) 
(X_train.shape, X_test.shape) 
((28273, 48, 48, 1), (7067, 48, 48, 1)) 
num_train = X_train.shape[0] 
num_test = X_test.shape[0] 
(num_train, num_test) 
(28273, 7067) 

Converting labels to categorical:

from keras.utils import np_utils # utilities for one-hot encoding of ground truth values 
Using TensorFlow backend. 
y_train = train_set.emotion 
y_train...