Python mlp.Classifier类代码示例

def mlp(number_layers, number_neurons_1, number_neurons_2, number_neurons_3, number_neurons_4, dropout_rate):

	layers = []
	number_neurons = []

	number_neurons.append(number_neurons_1)
	number_neurons.append(number_neurons_2)
	number_neurons.append(number_neurons_3)
	number_neurons.append(number_neurons_4)

	for i in np.arange(number_layers):
		layers.append(Layer("Sigmoid", units=number_neurons[i], dropout = dropout_rate))

	layers.append(Layer("Softmax",  units=2))

	scores = []

	for i in np.arange(n_validations):

		X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(X,Y, test_size=0.3, random_state=1)
	
		predictor = Classifier(
	    layers=layers,
	    learning_rate=0.001,
	    n_iter=25)

		predictor.fit(X_train, Y_train)

		scores.append(metrics.accuracy_score(Y_test, predictor.predict(X_test)))
	
	return -median(scores)

def main():
    vals, actions = matrixFromCSV("C:\\Users\\Chrisd\\Documents\\College\\Spring 2016\\379K\\Kaggle\\Kaggle\\train.csv")
    X_train, X_test, y_train, y_test = train_test_split(vals, actions, test_size=0.33, random_state=22)
    totalTest, totalAns = matrixFromCSV("C:\\Users\\Chrisd\\Documents\\College\\Spring 2016\\379K\\Kaggle\\Kaggle\\test.csv")


    nn = Classifier(
    layers=[
        Layer("Softmax", units=10),
        Layer("Linear", units=10),
        Layer("Sigmoid")],
    learning_rate=0.001,
    n_iter=20)

    nn.fit(X_train,y_train)
    pickle.dump(nn, open('nn.pkl', 'wb'))

    '''rs = RandomizedSearchCV(nn, param_distributions={
    'learning_rate': stats.uniform(0.001, 0.05),
    'hidden0__units': stats.randint(4, 100),
    'hidden1__units': stats.randint(4, 100),
    'hidden1__type': ["Linear","Rectifier", "Sigmoid", "Tanh"]})
    rs.fit(X_train, y_train)

    pickle.dump(rs, open('rs.pkl', 'wb'))
    rs = pickle.load(open('rs.pkl', 'rb'))'''

    #print(X_test.shape)
    #X_test.reshape(9,1)'''
    nn = pickle.load(open('nn.pkl', 'rb'))
    answer = nn.predict(X_test)
    writeToCSV(answer)
    print(getPercent(answer,y_test))

def train(X, ty):
    nn = Classifier(
        layers=[Layer("Sigmoid", units=5000), Layer("Sigmoid", units=5)], learning_rate=0.001, n_iter=100, verbose=1
    )
    nn.fit(X, ty)
    print "Train Done!"
    return nn

def autoEncoderOptimization(data):
	rbm = ae.AutoEncoder(
			layers=[
				ae.Layer("Tanh", units=300),
				ae.Layer("Sigmoid", units=200),
				ae.Layer("Tanh", units=100)
			],
			learning_rate=0.002,
			n_iter=10
		)

	rbm.fit(data["train"])

	model = Classifier(
			layers=[
				Layer("Tanh", units=300),
				Layer("Sigmoid", units=200),
				Layer("Tanh", units=100),
				Layer("Rectifier", units=100),
				Layer("Rectifier", units=50),
				Layer("Softmax")
			],
		)

	rbm.transfer(model)

	model.fit(data["train"], data["label"])

	prediction = model.predict(data["train"])

	print accuracy_score(data["label"], prediction)

def train_neural_network(samples, nn=None, learning_rate=0.001, n_iter=25): #pylint:disable=invalid-name
    """Trains a neural network using the given sample data.

    Args:
        samples: Tuple containing (sample inputs, sample outputs).
        nn: Neural network that should be trained. If this is none, a new NN
            will be created.
        learning_rate: Neural network learning rate.
        n_iter: Number of training iterations to use.

    Returns:
        The trained neural network.
    """
    sample_inputs, sample_outputs = check_samples(samples)

    # Create a new classifier if necessary.
    if nn is None:
        n_features = len(sample_inputs[0])
        nn = Classifier(
            layers=[
                Layer("Maxout", units=n_features, pieces=2),
                Layer("Softmax")],
            learning_rate=learning_rate,
            n_iter=n_iter)

    # Train the classifier.
    nn.fit(sample_inputs, sample_outputs)
    return nn

def batch_train(train,val,model_path):
    trainX,trainY = train
    valX,valY = val
    nn = Classifier(layers = [
			Convolution('Rectifier',
                                    channels=100,
                                    kernel_shape=(5,WORD_DIM),
                                    border_mode='valid'
                                    #pool_shape=(3,1),
                                    #pool_type='max'
                                    ),
			Layer('Rectifier',units=900,dropout=0.5),
                        Layer('Softmax')],
                        batch_size = 50,
                        learning_rate = 0.02,
                        normalize='dropout',
                        verbose = True)
    nn.n_iter = 100
    print 'Net created...'
    try:
	nn.fit(trainX,trainY)
    except KeyboardInterrupt:
	pickle.dump(nn,open(model_path,'wb'))
    pickle.dump(nn,open(model_path,'wb'))
    print 'Done, final model saved'
    print 'Testing'
    #Accuracy on the validation set
    print 'Validation accuracy:',batch_test(model_path,val)

 def test_VerboseClassifier(self):
     nn = MLPC(layers=[L("Softmax")], verbose=1, n_iter=1)
     a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,1), dtype=numpy.int32)
     nn.fit(a_in, a_out)
     assert_in("Epoch       Training Error       Validation Error       Time", self.buf.getvalue())
     assert_in("    1       ", self.buf.getvalue())
     assert_in("    N/A     ", self.buf.getvalue())

def trainMLP(trainX, trainY, validationX, validationY, activation='Tanh', algorithm='adam',
			 hidden_layer_size=2048, alpha=0.001):
	print('Learning...')

	trainX, trainY = shuffle(trainX, trainY)
	validationX, validationY = shuffle(validationX, validationY)

	mlp = Classifier(
		layers=[
			Layer(activation, units=hidden_layer_size, dropout=0.1),
			Layer("Softmax", units=len(np.unique(trainY)), dropout=0.2)
		], learning_rule=algorithm,
		learning_rate=0.0005,
		learning_momentum=0.9,
		batch_size=256,
		n_stable=10,
		n_iter=200,
		regularize="L2",
		weight_decay=alpha,
		loss_type="mcc", #?
		valid_set=(validationX, validationY),
		verbose=True)

	print(mlp)

	mlp.fit(trainX, trainY)

	return mlp

def wrapper_for_backprop_neural_network_code(train_x, train_y, test_x, test_y):
    score = None
    nn = Classifier(
            layers=[Layer('Sigmoid', units=5), 
            Layer('Softmax')], learning_rate=.001, n_iter=25)
    nn.fit(train_x, train_y)
    predicted = nn.predict(test_x)
    score = accuracy_score(predicted, test_y)
    return score

def fit_network():
	x,y = datasplit.data()
	x_normalized = normalize(x,norm='l2')
	nn = Classifier(layers=[Layer("Softmax" , units=1000),Layer("Softmax" , units=62)],learning_rate=0.02,n_iter=1)
	le= LabelEncoder()
	le.fit(y)
	y = le.transform(y)
	nn.fit(x_normalized , y)
	return nn

def _ann_n_iter(data, data_test, target, target_test, n_units):
    nn = Classifier(
        layers=[
            Layer("Sigmoid", units=n_units),
            Layer("Softmax")],
        n_iter=4000)
    nn.fit(data, target)
    test_score = nn.score(data_test, target_test)
    print n_units, test_score

def _ann_n_iter(data, data_test, target, target_test, n_iter):
    nn = Classifier(
        layers=[
            Layer("Sigmoid", units=100),
            Layer("Softmax")],
        n_iter=n_iter)
    train_score = np.mean(cross_validation.cross_val_score(nn, data, target, cv=10))
    nn.fit(data, target)
    test_score = nn.score(data_test, target_test)
    print n_iter, train_score, test_score

def CNN(X_train, y_train, X_test):
	nn = Classifier(
    layers=[
        Convolution("Rectifier", channels=20, kernel_shape=(5,5), dropout=0.25),
        Layer("Tanh", units=300),
        Layer("Tanh", units=100),
        Layer("Softmax")], learning_rate=0.02, n_iter=10)
	nn.fit(X_train, y_train)
	print('\nTRAIN SCORE', nn.score(X_train, y_train))
	return list(nn.predict(X_test))

def train_model(values,labels):
    model = Classifier(
	layers=[
		Convolution("Rectifier", channels=8, kernel_shape=(3,3)),
		Layer("Softmax")
	],
	learning_rate=0.02,
	n_iter=5)
    model.fit(values, labels)
    return model

def covnetTrain(train_bmi , train_labels , ite =10 , kernel =3 ,learn_rate =0.02, channel = 8):
    nn = Classifier(
        layers = [
            Convolution("Rectifier", channels=channel, kernel_shape=(kernel,kernel)),
            Layer("Softmax")],
        learning_rate=learn_rate,
        n_iter=ite
        )

    neuralnet = nn.fit(train_bmi , train_labels)
    return  neuralnet