"""Get functions""" print("complie: _train") _train = K.function(train_ins, [train_loss], updates=updates) print("complie: _train_with_acc") _train_with_acc = K.function(train_ins, [train_loss, train_accuracy], updates=updates) print("complie: _predict") _predict = K.function(predict_ins, [y_test], updates=state_updates) print("complie: _test") _test = K.function(test_ins, [test_loss]) print("complie: _test_with_acc") _test_with_acc = K.function(test_ins, [test_loss, test_accuracy]) model = Sequential() model.class_mode = "categorical" model._train = _train model._train_with_acc = _train_with_acc model._predict = _predict model._test = _test model._test_with_acc = _test_with_acc # Train the model each generation and show predictions against the validation dataset for iteration in range(1, 200): print() print("-" * 50) print("Iteration", iteration) model.fit( D_X_train, D_y_train, batch_size=BATCH_SIZE, nb_epoch=1, validation_data=(D_X_val, D_y_val), show_accuracy=True ) ### # Select 10 samples from the validation set at random so we can visualize errors