Python Model примеры использования

Пример #1

Файл: chain_rnn.py Проект: lchmo444/dlx

tdd = U.TimeDistributedDense(output_length, hidden_dim, output_dim, 'TDD')
#Activation
activation = U.Activation('softmax')
#Output layer
output = U.Output()
#Output layer
mask_out = U.Output()
'''Define Relations'''
rnn.set_input('input_sequence', data, 'output')
rnn.set_input('input_mask', mask_in, 'output')
tdd.set_input('input', rnn, 'output_sequence')
activation.set_input('input', tdd, 'output')
output.set_input('input', activation, 'output')
mask_out.set_input('input', mask_in, 'output')
'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'Y')
model.add_input(mask_in, 'MASK_IN')
model.add_output(mask_out, 'MASK_OUT')
model.add_hidden(rnn)
model.add_hidden(tdd)
model.add_hidden(activation)

model.compile(optimizer='adam',
              loss_configures=[
                  ('Y', 'categorical_crossentropy', 'MASK_OUT', False,
                   "categorical"),
              ],
              verbose=0)

Пример #2

Файл: addition_attention_without_encoder.py Проект: lchmo444/dlx

activation = U.Activation('softmax')
#Output layer
output_y = U.Output()
output_alpha = U.Output()
'''Define Relations'''
add1.set_input('input', dataA, 'output')
decoder.set_input('input_sequence', add1, 'output')
decoder.set_input('context', dataB, 'output')
one2many.set_input('input', decoder, 'output_sequence_with_alpha')
remove1.set_input('input', one2many, 'y')
tdd.set_input('input', remove1, 'output')
activation.set_input('input', tdd, 'output')
output_y.set_input('input', activation, 'output')
output_alpha.set_input('input', one2many, 'alpha')
'''Build Model'''
model = Model()
model.add_input(dataA, 'A')
model.add_input(dataB, 'B')
model.add_output(output_y, ['y'])
model.add_output(output_alpha, ['alpha'])
model.add_hidden(decoder)
model.add_hidden(one2many)
model.add_hidden(add1)
model.add_hidden(remove1)
model.add_hidden(tdd)
model.add_hidden(activation)

model.compile(optimizer='adam',
              loss_configures=[
                  ('y', 'categorical_crossentropy', None, False,
                   "categorical"),

Пример #3

Файл: addition_rnn_save.py Проект: lchmo444/dlx

decoder = R.RNN(output_length, hidden_dim, hidden_dim, name='DECODER')
#decoder = R.LSTM(output_length, hidden_dim, hidden_dim, name='DECODER')
#Time Distributed Dense
tdd = U.TimeDistributedDense(output_length, hidden_dim, output_dim, 'TDD')
#Activation
activation = U.Activation('softmax')
#Output layer
output = U.Output()
'''Define Relations'''
encoder.set_input('input_sequence', data, 'output')
decoder.set_input('input_single', encoder, 'output_last')
tdd.set_input('input', decoder, 'output_sequence')
activation.set_input('input', tdd, 'output')
output.set_input('input', activation, 'output')
'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'y')
model.add_hidden(encoder)
model.add_hidden(decoder)
model.add_hidden(tdd)
model.add_hidden(activation)

model.compile(optimizer='adam',
              loss_configures=[
                  ('y', 'categorical_crossentropy', None, False,
                   "categorical"),
              ],
              verbose=0)

score = model.evaluate(data={

Пример #4

Файл: addition_rnn_save.py Проект: lxastro/dlx

#Time Distributed Dense
tdd = U.TimeDistributedDense(output_length, hidden_dim, output_dim, 'TDD')
#Activation
activation = U.Activation('softmax')
#Output layer
output = U.Output()

'''Define Relations'''
encoder.set_input('input_sequence', data, 'output')
decoder.set_input('input_single', encoder, 'output_last')
tdd.set_input('input', decoder, 'output_sequence')
activation.set_input('input', tdd, 'output')
output.set_input('input', activation, 'output')

'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'y')
model.add_hidden(encoder)
model.add_hidden(decoder)
model.add_hidden(tdd)
model.add_hidden(activation)


model.compile(optimizer='adam', loss_configures = [('y', 'categorical_crossentropy', None, False, "categorical"),], verbose=0)

score = model.evaluate(data = {'X': D_X_val, 
                           'y': D_y_val},
                       show_accuracy=True, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])

Пример #5

Файл: chain_rnn.py Проект: lxastro/dlx

activation = U.Activation('softmax')
#Output layer
output = U.Output()
#Output layer
mask_out = U.Output()

'''Define Relations'''
rnn.set_input('input_sequence', data, 'output')
rnn.set_input('input_mask', mask_in, 'output')
tdd.set_input('input', rnn, 'output_sequence')
activation.set_input('input', tdd, 'output')
output.set_input('input', activation, 'output')
mask_out.set_input('input', mask_in, 'output')

'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'Y')
model.add_input(mask_in, 'MASK_IN')
model.add_output(mask_out, 'MASK_OUT')
model.add_hidden(rnn)
model.add_hidden(tdd)
model.add_hidden(activation)

model.compile(optimizer='adam', loss_configures = [('Y', 'categorical_crossentropy', 'MASK_OUT', False, "categorical"),], verbose=0)

score = model.evaluate(data = {'X': D_X_val,
                               'MASK_IN': D_mask_val, 
                               'Y': D_Y_val},
                       batch_size=BATCH_SIZE, 
                       show_accuracy=True, verbose=0)

Пример #6

Файл: addition_attention.py Проект: lxastro/dlx

#Output layer
output_y = U.Output()
output_alpha = U.Output()

'''Define Relations'''
encoder.set_input('input_sequence', data, 'output')
decoder.set_input('input_single', encoder, 'output_last')
decoder.set_input('context', data, 'output')
one2many.set_input('input', decoder, 'output_sequence_with_alpha')
tdd.set_input('input', one2many, 'y')
activation.set_input('input', tdd, 'output')
output_y.set_input('input', activation, 'output')
output_alpha.set_input('input', one2many, 'alpha')

'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output_y, ['y'])
model.add_output(output_alpha, ['alpha'])
model.add_hidden(encoder)
model.add_hidden(decoder)
model.add_hidden(one2many)
model.add_hidden(tdd)
model.add_hidden(activation)


model.compile(optimizer='adam', loss_configures = [('y', 'categorical_crossentropy', None, False, "categorical"),], verbose=2)

score = model.evaluate(data = {'X': D_X_val, 
                           'y': D_y_val},
                       show_accuracy=True, verbose=0)

Пример #7

Файл: mnist_mlp.py Проект: lchmo444/dlx

#Dense unit 3
dense_3 = U.Dense(hidden_size, output_size, 'Dense3')
#Activation unit 3
activation_3 = U.Activation('softmax')
#Output unit
output = U.Output()
'''Define Relations'''
dense_1.set_input('input', data, 'output')
activation_1.set_input('input', dense_1, 'output')
dense_2.set_input('input', activation_1, 'output')
activation_2.set_input('input', dense_2, 'output')
dense_3.set_input('input', activation_2, 'output')
activation_3.set_input('input', dense_3, 'output')
output.set_input('input', activation_3, 'output')
'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'y')
model.add_hidden(dense_1)
model.add_hidden(dense_2)
model.add_hidden(dense_3)
model.add_hidden(activation_1)
model.add_hidden(activation_2)
model.add_hidden(activation_3)

model.compile(optimizer='rmsprop',
              loss_configures=[
                  ('y', 'categorical_crossentropy', None, False,
                   "categorical"),
              ],
              verbose=0)

Пример #8

Файл: mnist_mlp.py Проект: lxastro/dlx

#Activation unit 3
activation_3 = U.Activation('softmax')
#Output unit
output = U.Output()

'''Define Relations'''
dense_1.set_input('input', data, 'output')
activation_1.set_input('input', dense_1, 'output')
dense_2.set_input('input', activation_1, 'output')
activation_2.set_input('input', dense_2, 'output')
dense_3.set_input('input', activation_2, 'output')
activation_3.set_input('input', dense_3, 'output')
output.set_input('input', activation_3, 'output')

'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'y')
model.add_hidden(dense_1)
model.add_hidden(dense_2)
model.add_hidden(dense_3)
model.add_hidden(activation_1)
model.add_hidden(activation_2)
model.add_hidden(activation_3)


model.compile(optimizer='rmsprop', loss_configures = [('y', 'categorical_crossentropy', None, False, "categorical"),], verbose=0)
model.fit(data = {'X': D_X_train, 
                  'y': D_Y_train},
          batch_size=batch_size, 
          nb_epoch=nb_epoch,

Пример #9

Файл: addition_rnn_load.py Проект: lchmo444/dlx

decoder = R.RNN(output_length, hidden_dim, hidden_dim, name='DECODER')
#decoder = R.LSTM(output_length, hidden_dim, hidden_dim, name='DECODER')
#Time Distributed Dense
tdd = U.TimeDistributedDense(output_length, hidden_dim, output_dim, 'TDD')
#Activation
activation = U.Activation('softmax')
#Output layer
output = U.Output()
'''Define Relations'''
encoder.set_input('input_sequence', data, 'output')
decoder.set_input('input_single', encoder, 'output_last')
tdd.set_input('input', decoder, 'output_sequence')
activation.set_input('input', tdd, 'output')
output.set_input('input', activation, 'output')
'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'y')
model.add_hidden(encoder)
model.add_hidden(decoder)
model.add_hidden(tdd)
model.add_hidden(activation)

model.compile(optimizer='adam',
              loss_configures=[
                  ('y', 'categorical_crossentropy', None, False,
                   "categorical"),
              ],
              verbose=0)

import os

Пример #10

Файл: addition_rnn_load.py Проект: lxastro/dlx

#Time Distributed Dense
tdd = U.TimeDistributedDense(output_length, hidden_dim, output_dim, 'TDD')
#Activation
activation = U.Activation('softmax')
#Output layer
output = U.Output()

'''Define Relations'''
encoder.set_input('input_sequence', data, 'output')
decoder.set_input('input_single', encoder, 'output_last')
tdd.set_input('input', decoder, 'output_sequence')
activation.set_input('input', tdd, 'output')
output.set_input('input', activation, 'output')

'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'y')
model.add_hidden(encoder)
model.add_hidden(decoder)
model.add_hidden(tdd)
model.add_hidden(activation)


model.compile(optimizer='adam', loss_configures = [('y', 'categorical_crossentropy', None, False, "categorical"),], verbose=0)

import os
usr_home = os.path.expanduser('~')
save_dir = os.path.join(usr_home, ".dlx/saved_model/")
model_path = os.path.join(save_dir, "addition_rnn_h128_i200.dlx")
 

Пример #11

Файл: action_rec_rnn.py Проект: lxastro/lx_action_rec

tdd = U.TimeDistributedDense(length, n_hidden, n_class, 'TDD')
tdm = U.TimeDistributedMerge('ave')
#Activation
activation = U.Activation('softmax')
#Output layer
output = U.Output()

'''Define Relations'''
rnn.set_input('input_sequence', data, 'output')
tdd.set_input('input', rnn, 'output_sequence')
tdm.set_input('input', tdd, 'output')
activation.set_input('input', tdm, 'output')
output.set_input('input', activation, 'output')

'''Build Model'''
model = Model()
model.add_input(data, 'X')
model.add_output(output, 'y')
model.add_hidden(rnn)
model.add_hidden(tdd)
model.add_hidden(tdm)
model.add_hidden(activation)


model.compile(optimizer='rmsprop', loss_configures = [('y', 'categorical_crossentropy', None, False, "categorical"),], verbose=0)

score = model.evaluate(data = {'X': test.feature, 
                           'y': test.label},
                       show_accuracy=True, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])

Пример #12

Файл: addition_attention_without_encoder.py Проект: lxastro/dlx

output_y = U.Output()
output_alpha = U.Output()

"""Define Relations"""
add1.set_input("input", dataA, "output")
decoder.set_input("input_sequence", add1, "output")
decoder.set_input("context", dataB, "output")
one2many.set_input("input", decoder, "output_sequence_with_alpha")
remove1.set_input("input", one2many, "y")
tdd.set_input("input", remove1, "output")
activation.set_input("input", tdd, "output")
output_y.set_input("input", activation, "output")
output_alpha.set_input("input", one2many, "alpha")

"""Build Model"""
model = Model()
model.add_input(dataA, "A")
model.add_input(dataB, "B")
model.add_output(output_y, ["y"])
model.add_output(output_alpha, ["alpha"])
model.add_hidden(decoder)
model.add_hidden(one2many)
model.add_hidden(add1)
model.add_hidden(remove1)
model.add_hidden(tdd)
model.add_hidden(activation)


model.compile(
    optimizer="adam", loss_configures=[("y", "categorical_crossentropy", None, False, "categorical")], verbose=0
)