def DigitRecognitionTest(): X_train, y_train, X_val, y_val, X_test, y_test = load_and_extract_mnist_data.load_dataset() Y_train = [] for e in y_train: tmp = [0]*10 tmp[e]=1 Y_train.append(tmp) Y_train = np.array(Y_train) Y_val = [] for e in y_val: tmp = [0]*10 tmp[e]=1 Y_val.append(tmp) Y_val = np.array(Y_val) Y_test = [] for e in y_test: tmp = [0]*10 tmp[e]=1 Y_test.append(tmp) Y_test = np.array(Y_test) myann = LeNet5() myann.setTrain(X_train,Y_train) myann.setCVD(X_val,Y_val) myann.setTest(X_test,Y_test) #myann.MiniBatch_Train(64,1000,ifshow=True,gradiantCheck=False) #use cross validation data to test myann.Final_Train_and_Evaluate(64,1000,ifshow=True) #use test data to test
def DigitRecognitionTest(): X_train, y_train, X_val, y_val, X_test, y_test = load_and_extract_mnist_data.load_dataset() X_train = X_train.reshape(np.size(X_train,0),28*28).transpose() X_val = X_val.reshape(np.size(X_val,0),28*28).transpose() X_test = X_test.reshape(np.size(X_test,0),28*28).transpose() Y_train = [] for e in y_train: tmp = [0]*10 tmp[e]=1 Y_train.append(tmp) Y_train = np.array(Y_train).transpose() Y_val = [] for e in y_val: tmp = [0]*10 tmp[e]=1 Y_val.append(tmp) Y_val = np.array(Y_val).transpose() Y_test = [] for e in y_test: tmp = [0]*10 tmp[e]=1 Y_test.append(tmp) Y_test = np.array(Y_test).transpose() myann = NeuralNetwork('Digital Recognition') myann.model.SetInputLayer(28*28) myann.model.SetOutputLayer('SoftMax') myann.model.SetFullConnectLayer([(50,'ReLu'),(20,'ReLu'),(20,'ReLu'),(10,None)]) myann.setTrain(X_train,Y_train) myann.setCVD(X_val,Y_val) myann.setTest(X_test,Y_test) myann.MiniBatch_Train(1000,2500,True)
def DigitRecognitionTest(): X_train, y_train, X_val, y_val, X_test, y_test = load_and_extract_mnist_data.load_dataset( ) Y_train = [] for e in y_train: tmp = [0] * 10 tmp[e] = 1 Y_train.append(tmp) Y_train = np.array(Y_train) Y_val = [] for e in y_val: tmp = [0] * 10 tmp[e] = 1 Y_val.append(tmp) Y_val = np.array(Y_val) Y_test = [] for e in y_test: tmp = [0] * 10 tmp[e] = 1 Y_test.append(tmp) Y_test = np.array(Y_test) myann = LeNet5() myann.setTrain(X_train, Y_train) myann.setCVD(X_val, Y_val) myann.setTest(X_test, Y_test) #myann.MiniBatch_Train(64,1000,ifshow=True,gradiantCheck=False) #use cross validation data to test myann.Final_Train_and_Evaluate(64, 1000, ifshow=True) #use test data to test
def main_data(): X_train, y_train, X_val, y_val, X_test, y_test = im.load_dataset() m_Xtrain = my_data(X_train) ##### (50000,785) m_Xval = my_data(X_val) ##### (10000, 785) m_Xtest = my_data(X_test) #### (10000, 785) mm_xtrain = m_Xtrain[0:5000, :] mm_ytrain = y_train[0:5000] mm_xval = m_Xval[0:1000, :] mm_yval = y_val[0:1000] train_y = y_lab(mm_ytrain) val_y = y_lab(mm_yval) return mm_xtrain, train_y, mm_xval, val_y
"type": "relu" }, { "type": "fc", "neurons": n_classes }, { "type": "relu" }, { "type": "softmax", "categories": n_classes } ]) X_train, y_train, X_val, y_val, X_test, y_test = im.load_dataset() mm_xtrain, mm_ytrain, mm_xval, mm_yval = proce.main_data() """ train_data, test_data, train_target, test_target = train_test_split(mm_xtrain[0:3000,], mm_ytrain[0:3000], train_size=0.7) train_data = train_data.reshape((len(train_data),1, 28, 28)) test_data = test_data.reshape((len(test_data), 1, 28, 28)) """ train_data = X_train test_data = X_val train_target = mm_ytrain test_target = mm_yval #print test_data.shape, test_target.shape net.train(train_data, train_target, n_epochs=1000, batch_size=64) """ ###Parallel python cpu