本文整理汇总了Python中nolearn.lasagne.NeuralNet.load_weights_from方法的典型用法代码示例。如果您正苦于以下问题：Python NeuralNet.load_weights_from方法的具体用法？Python NeuralNet.load_weights_from怎么用？Python NeuralNet.load_weights_from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nolearn.lasagne.NeuralNet的用法示例。
在下文中一共展示了NeuralNet.load_weights_from方法的4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: GlorotUniform
# 需要导入模块: from nolearn.lasagne import NeuralNet [as 别名]
# 或者: from nolearn.lasagne.NeuralNet import load_weights_from [as 别名]
dropout_p=0.5,
output_num_units=num_classes, output_nonlinearity=lasagne.nonlinearities.softmax,
output_W = GlorotUniform(gain = 1.0),
# ----------------------- ConvNet Params -------------------------------------------
update = nesterov_momentum,
update_learning_rate = learning_rate,
update_momentum = momentum,
max_epochs = num_epochs,
verbose = 1,
)
tic = time.time()
for i in range(12):
convNet.fit(dataset['X_train'], dataset['Y_train'])
fl = './model1/saved_model_data' + str(i+1) + '.npz'
convNet.save_weights_to(fl)
print 'Model saved to file :- ', fl
toc = time.time()
fl = './model1/saved_model_data' + str(6) + '.npz'
convNet.load_weights_from(fl)
y_pred = convNet.predict(dataset['X_test'])
print classification_report(Y_test, y_pred)
print accuracy_score(Y_test, y_pred)
print 'Time taken to train the data :- ', toc-tic, 'seconds'
示例2: AdjustVariable
# 需要导入模块: from nolearn.lasagne import NeuralNet [as 别名]
# 或者: from nolearn.lasagne.NeuralNet import load_weights_from [as 别名]
update_learning_rate=theano.shared(float32(0.05)),
update_momentum=theano.shared(float32(0.9)),
regression=True,
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=0.05, stop=0.0001),
AdjustVariable('update_momentum', start=0.9, stop=0.999),
],
batch_iterator_train=BatchIterator(batch_size=128),
max_epochs=1200,
verbose=1,
)
pretrain_net=joblib.load('pretrain_net.pkl')
final_net.load_weights_from(pretrain_net)
no_of_examples_ground_truth=np.load('no_of_examples_ground_truth.npy')
X2=np.memmap('XG_mat.npy', dtype='float32', mode='r', shape=(no_of_examples_ground_truth,3,image_size,image_size))
y2=np.memmap('yG_mat.npy', dtype='float32', mode='r', shape=(no_of_examples_ground_truth,map_size*map_size))
final_net.fit(X2,y2)
joblib.dump(final_net, 'final_net.pkl')
###############################################################################
y=final_net.predict(X2)
sio.savemat('prediction_vector.mat', {'y':y})
示例3: recunstruct_cae
# 需要导入模块: from nolearn.lasagne import NeuralNet [as 别名]
# 或者: from nolearn.lasagne.NeuralNet import load_weights_from [as 别名]
def recunstruct_cae(folder_path):
cnn = NeuralNet()
cnn.load_params_from(folder_path + CONV_AE_PARAMS_PKL)
cnn.load_weights_from(folder_path + CONV_AE_NP)
return cnn示例4: AdjustVariable
# 需要导入模块: from nolearn.lasagne import NeuralNet [as 别名]
# 或者: from nolearn.lasagne.NeuralNet import load_weights_from [as 别名]
('dense1', DenseLayer),
('dropout1', DropoutLayer),
('dense2', DenseLayer),
('dropout2', DropoutLayer),
('dense3', DenseLayer),
('output', DenseLayer)],
input_shape=(None, num_features),
dense1_num_units=512,
dropout1_p=0.5,
dense2_num_units=512,
dropout2_p=0.5,
dense3_num_units=512,
output_num_units=num_classes,
output_nonlinearity=softmax,
update=nesterov_momentum,
eval_size=0.2,
verbose=1,
update_learning_rate=theano.shared(float32(0.01)),
update_momentum=theano.shared(float32(0.9)),
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=0.01, stop=0.00001),
AdjustVariable('update_momentum', start=0.9, stop=0.999),
EarlyStopping(),
],
max_epochs=10000,)
net0.initialize()
# do_fit(net0, 'data/train_impu_norm_shuf.csv', n_iter=1)
net0.load_weights_from('nn_weights')
RainCompetition.do_predict(net0, RainCompetition.__data__['test_normalized'], 'data/rain_nn_pred.csv')