X_train = X[train_idx]
X_test = X[test_idx]
inp = Input((1, img_rows, img_cols))
model = custom_model(inp, n_classes=n_class)
#model = custom_model2(inp, n_classes=n_class)
model.compile(loss='categorical_crossentropy',
metrics=['accuracy'],
optimizer='Adadelta')
model.fit(X_train,
y[train_idx],
batch_size=cm.bs,
epochs=cm.n_ep,
verbose=0,
callbacks=[cm.custom_stopping(value=cm.loss, verbose=1)],
validation_data=(X_train, y[train_idx]))
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_true = np.argmax(y[test_idx], axis=1)
acc_fold = accuracy_score(y_true, y_pred)
avg_acc.append(acc_fold)
recall_fold = recall_score(y_true, y_pred, average='macro')
avg_recall.append(recall_fold)
f1_fold = f1_score(y_true, y_pred, average='macro')
avg_f1.append(f1_fold)
print('Chen and Xue 2015 {}'.format(data_input_file))
for i in range(0, len(folds)):
train_idx = folds[i][0]
test_idx = folds[i][1]
X_train = X[train_idx]
X_test = X[test_idx]
inp = Input((1, img_rows, img_cols))
model = custom_model(inp, n_classes=n_class)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='Adadelta')
model.fit(X_train, y[train_idx], batch_size=cm.bs, epochs=cm.n_ep,
verbose=0, callbacks=[cm.custom_stopping(value=cm.loss, verbose=1)], validation_data=(X_train, y[train_idx]))
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_true = np.argmax(y[test_idx], axis=1)
acc_fold = accuracy_score(y_true, y_pred)
avg_acc.append(acc_fold)
recall_fold = recall_score(y_true, y_pred, average='macro')
avg_recall.append(recall_fold)
f1_fold = f1_score(y_true, y_pred, average='macro')
avg_f1.append(f1_fold)