def fit2():
print("fit2")
model = Model([
nn.Dense(128, inshape=1, activation='relu'),
nn.Dense(256, activation='relu'),
nn.Dropout(0.80),
nn.Dense(1)
])
model.assemble()
sess = Session(
model,
loss=losses.Mse(),
optimizer=optimizers.Fixed(),
)
history = sess.fit(ds,
200000,
val_data=val_ds,
val_epochs=1000,
listeners=[
FitListener('val_end',
callback=lambda h: on_val_end(sess, h))
])
fit_report(history, report_path + '02.png', 15)
def setUpClass(cls):
model = Model(
[Simplelayer(4, inshape=3),
Simplelayer(5),
Simplelayer(1)])
model.assemble()
cls.sess = Session(model,
loss=losses.Mse(),
optimizer=optimizers.Fixed())
def setUpClass(cls):
model = Model([
cnn.Conv2D(3, (3,3), inshape=(2,12,12)),
cnn.MaxPool2D(),
cnn.Conv2D(4, (5, 5), padding='valid'),
nn.Flatten(),
nn.Dense(10)
])
model.assemble()
cls.model = model
def fit_2():
model = Model(
[nnlys.Dense(32, inshape=1, activation='relu'),
nnlys.Dense(1)])
model.assemble()
#使用了relu激活函数模型变成了非线性的, 增加了模型的复杂度
history = train(30000, ds_0, model, record_epochs=300)
history['loss'] = history['loss'][5:]
history['val_loss'] = history['val_loss'][5:]
history['epochs'] = history['epochs'][5:]
fit_report(history, report_path + '02.png')
def fit_lstm():
print("fit lstm")
model = Model([
rnn.Embedding(64, vocab_size+1),
wrapper.Bidirectional(rnn.LSTM(128), rnn.LSTM(128)),
nn.Filter(),
nn.Dense(64),
nn.Dropout(0.5),
nn.Dense(1, activation='linear')
])
model.assemble()
fit('lstm', model)
def fit_gru():
print("fit gru")
model = Model([
rnn.Embedding(64, vocab_size+1),
wrapper.Bidirectional(rnn.GRU(64), rnn.GRU(64)),
nn.Filter(),
nn.Dense(64),
nn.Dropout(0.5),
nn.Dense(1, activation='linear')
])
model.assemble()
fit('gru', model)
def fit_gru():
vocab_size = vocab.size()
print("vocab size: ", vocab_size)
model = Model([
rnn.Embedding(256, vocab_size),
rnn.GRU(1024, stateful=True),
nn.Dense(1024),
nn.Dropout(0.5),
nn.Dense(vocab_size, activation='linear')
])
model.assemble()
fit("gru", model)
def fit_4():
model = Model([
nnlys.Dense(128, inshape=1, activation='relu'),
nnlys.Dense(64, activation='relu'),
nnlys.Dense(1)
])
model.assemble()
history = train(30000, ds_1, model, batch_size=16, record_epochs=300)
history['loss'] = history['loss'][20:]
history['val_loss'] = history['val_loss'][20:]
history['epochs'] = history['epochs'][20:]
fit_report(history, report_path + '04.png')
def fit(name, optimizer):
inshape = ds_train.data.shape[1:]
#pdb.set_trace()
model = Model([
cnn.Conv2D(32, (3, 3), inshape=inshape),
cnn.MaxPool2D((2, 2), strides=(2, 2)),
cnn.Conv2D(64, (3, 3)),
cnn.MaxPool2D((2, 2), strides=(2, 2)),
cnn.Conv2D(64, (3, 3)),
nn.Flatten(),
nn.Dense(64),
nn.Dropout(0.5),
nn.Dense(10)
])
model.assemble()
sess = Session(model,
loss=losses.CategoricalCrossentropy(),
optimizer=optimizer)
stop_fit = session.condition_callback(lambda: sess.stop_fit(), 'val_loss',
30)
accuracy = lambda h: fit_tools.accuracy(sess, ds_test, h)
def save_and_report(history):
#pdb.set_trace()
fit_tools.fit_report(history, report_path + name + ".png")
model.save(model_path + name)
#pdb.set_trace()
history = sess.fit(ds_train,
200,
val_data=ds_test,
val_steps=100,
listeners=[
stop_fit,
session.FitListener('val_end', callback=accuracy),
session.FitListener('val_end',
callback=save_and_report)
])
save_and_report(history)
def fit():
inshape = ds_train.data.shape[1]
model = Model([nn.Dense(10, inshape=inshape, activation='relu')])
model.assemble()
sess = Session(model,
loss=losses.CategoricalCrossentropy(),
optimizer=optimizers.Fixed(0.001))
stop_fit = session.condition_callback(lambda: sess.stop_fit(), 'val_loss',
10)
#pdb.set_trace()
history = sess.fit(ds_train,
20000,
val_epochs=5,
val_data=ds_test,
listeners=[
stop_fit,
session.FitListener('val_end', callback=accuracy)
])
fit_report(history, report_path + "0.png")
def fit_1():
model = Model([nnlys.Dense(32, inshape=1), nnlys.Dense(1)])
model.assemble()
#这个模型是一个线性模型, 用来拟合非线性函数, 模型复杂度不够，一定会表现出欠拟合
history = train(20000, ds_0, model, record_epochs=100)
fit_report(history, report_path + '01.png')