train_data = np.genfromtxt('data/train_data.csv', delimiter=',') train_labels = np.genfromtxt('data/train_labels.csv', delimiter=',') # Standardize data scaler = preprocessing.StandardScaler() scaler.fit(train_data) X = scaler.transform(train_data) y = train_labels model = Sequential() model.add(Dense(units=64, activation='relu' input_dim=100)) model.add(Dense(units=10, activation='softmax')) model.compiler(loss='categorial_crossentropy', optimizer='sgd', metrics=['accuracy']) model.fit(X, y, epocs=5, batch_size=32) loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128) # best_score = 0.0 # for j in [10,20,40,80,150,270]: # # Use PCA to reduce dimensionality # print ('--------------------------') # if j == 270: # Xn = X # print ('Using entire original data') # else: