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(name, model):
sess = Session(model,
loss = losses.SparseCategoricalCrossentropy(),
optimizer = optimizers.Adam()
)
stop_fit = session.condition_callback(lambda :sess.stop_fit(), 'loss', 100)
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, 1000,
listeners=[
stop_fit,
session.FitListener('epoch_end', callback=save_and_report)
]
)
save_and_report(history)
def fit(name, model):
sess = Session(model,
loss = losses.BinaryCrossentropy(),
optimizer = optimizers.Adam()
)
stop_fit = session.condition_callback(lambda :sess.stop_fit(), 'val_loss', 10)
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, 100, val_data=ds_test, val_batches=20,
listeners=[
stop_fit,
session.FitListener('val_end', callback=fit_tools.binary_accuracy),
session.FitListener('epoch_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")