本文整理汇总了Python中sklearn.neural_network.MLPClassifier.threshold_update方法的典型用法代码示例。如果您正苦于以下问题：Python MLPClassifier.threshold_update方法的具体用法？Python MLPClassifier.threshold_update怎么用？Python MLPClassifier.threshold_update使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neural_network.MLPClassifier的用法示例。
在下文中一共展示了MLPClassifier.threshold_update方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from sklearn.neural_network import MLPClassifier [as 别名]
# 或者: from sklearn.neural_network.MLPClassifier import threshold_update [as 别名]
#.........这里部分代码省略.........
help="file name to write parameter updates to",)
parser.add_option("-l", "--layernum",
dest="layer_num",
default="0",
help="specifies the layer that the update weight is randomly sampled between, for the input layer, use 0")
parser.add_option("-t", "--trainsize",
dest="training_size",
default="30000",
help="specifies the training size")
parser.add_option("-s", "--testsize",
dest="test_size",
default="5000",
help="specifies the training size")
parser.add_option("-d", "--threshold",
dest="threshold",
default="0",
help="specifies the dropout threshold for updates to weights, e.g. -d 1e-5")
parser.add_option("-p", "--percentdropout",
dest="dropout_percentage",
default="15",
help="specifies the dropout chance for updates to weights, e.g. -p 15")
(options, args) = parser.parse_args()
print options
print args
#######################################
########### HYPERPARAMETERS ###########
#######################################
training_size = int(options.training_size)
test_size = int(options.test_size)
hidden_layers = tuple(eval(options.layers))
max_iteration = 100
tolerance = 1e-4
batch_size = 1
#######################################
########## FETCH MNIST DATA ###########
#######################################
mnist = fetch_mldata("MNIST original")
# rescale the data, use the traditional train/test split
X, y = mnist.data / 255., mnist.target
X_train, X_test = X[:training_size], X[training_size:training_size + test_size]
y_train, y_test = y[:training_size], y[training_size:training_size + test_size]
# Validate shape of training and test matrices
print X_train.shape, y_train.shape, X_test.shape, y_test.shape
#######################################
####### RANDOM WEIGHT SELECTION #######
#######################################
layer_num = int(options.layer_num)
if layer_num > len(hidden_layers):
layer_num = 0
if layer_num == -1:
print "Last layer used (layer between last hidden layer and output layer)"
layer_num = len(hidden_layers)
layer_sizes_array = [X_train.shape[1]] + list(hidden_layers) + [len(set(y_train))]
print layer_sizes_array
layer_num_array = [layer_num, random.randint(0, layer_sizes_array[layer_num] - 1), random.randint(0, layer_sizes_array[layer_num + 1] - 1)]
#######################################
######### TRAIN AND FIT MODEL #########
#######################################
mlp = MLPClassifier(hidden_layer_sizes=hidden_layers, alpha=1e-4, max_iter=max_iteration,
algorithm='sgd', verbose=10, tol=tolerance, random_state=1, batch_size=batch_size)
mlp.out_file_name = path + options.update_file
mlp.layer_num = layer_num_array
mlp.threshold_update = float(options.threshold)
mlp.dropout_percentage = int(options.dropout_percentage)
try:
os.remove(path + options.update_file)
except:
1
mlp.fit(X_train, y_train)
print("Training set score: %f" % mlp.score(X_train, y_train))
print("Test set score: %f" % mlp.score(X_test, y_test))
f = open(mlp.out_file_name, 'r')
lines = f.readlines()
print "Lines are read"
arr = np.asarray(map(lambda x: float(x), lines))
numerator = len(filter(lambda x: abs(x) < 1e-5, arr))
percent_negligible = numerator/ float(len(arr))
print "Percent neglible:", str(percent_negligible)
p1 = plt.plot([i for i in range(len(arr))], arr)
plt.show()