def getModel(compile_ = True):
modelpath = MODELPATH
modelname = MODELNAME
if not os.path.exists(modelpath):
os.mkdir(modelpath)
modelpath = os.path.join(modelpath,modelname)
if not os.path.exists(modelpath):
os.mkdir(modelpath)
x_transform = [Normalize(0.0033127920560417023,0.012673124326485102)]
y_transform = [cutOut([96,160,96,160])]
train,test = getData(BATCH_SIZE,
DIMENSION,CHANNELS,
timeToPred=30,
area_=cutOut([96,160,96,160]),
y_transform=y_transform,
x_transform=x_transform)
model = ZeroInflated_negativ_Binomial((*DIMENSION,CHANNELS))
if compile_ == False:
return model,modelpath,train,test
def NLL(y_true, y_hat):
return -y_hat.log_prob(y_true)
model.compile(loss=NLL,
optimizer=Adam( lr= 1e-3 ))
model.summary()
modelpath_h5 = os.path.join(modelpath,
modelname+'-{epoch:03d}-{loss:03f}-{val_loss:03f}.h5')
checkpoint = ModelCheckpoint(modelpath_h5,
verbose=0,
monitor='val_loss',
save_best_only=True,
mode='auto')
return model,checkpoint,modelpath,train,test
def getModel(compile_=True):
modelpath = MODELPATH
modelname = MODELNAME
if not os.path.exists(modelpath):
os.mkdir(modelpath)
modelpath = os.path.join(modelpath,modelname)
if not os.path.exists(modelpath):
os.mkdir(modelpath)
#x_transform = [Normalize(0.0033127920560417023,0.012673124326485102)]
#x_transform = [LogBin()]
y_transform = [cutOut([16,80,16,80]),LogBin()]
#y_transform = [cutOut([96,160,96,160]),LogBin()]
train,test = getData(BATCH_SIZE,
DIMENSION,CHANNELS,
timeToPred=30,
y_transform=y_transform)
model = six_class_categorical((*DIMENSION,CHANNELS))
if compile_ == False:
return model,modelpath,train,test
neg_log_likelihood = lambda x, rv_x: tf.math.reduce_mean(-rv_x.log_prob(x))
def NLL(y_true, y_hat):
return -y_hat.log_prob(y_true)
model.compile(loss=NLL,
optimizer=Adam( lr= 1e-4 ))
model.summary()
modelpath_h5 = os.path.join(modelpath,
modelname+'-{epoch:03d}-{loss:03f}-{val_loss:03f}.h5')
checkpoint = ModelCheckpoint(modelpath_h5,
verbose=0,
monitor='val_loss',
save_best_only=True,
mode='auto')
return model,checkpoint,modelpath,train,test
def getModel():
modelpath = MODELPATH
modelname = MODELNAME
if not os.path.exists(modelpath):
os.mkdir(modelpath)
modelpath = os.path.join(modelpath,modelname)
if not os.path.exists(modelpath):
os.mkdir(modelpath)
input_shape = (*DIMENSION,CHANNELS)
model = ZeroInflated_negativ_Binomial(
input_shape=input_shape
)
y_transform = [cutOut([7,57,7,57])]
train,test = getData(BATCH_SIZE,
DIMENSION,CHANNELS,
timeToPred=10,
y_transform=y_transform)
def NLL(y_true, y_hat):
return -y_hat.log_prob(y_true)
#neg_log_likelihood = lambda x, rv_x: tf.math.reduce_mean(-rv_x.log_prob(x))
model.compile(loss=NLL,
optimizer=Adam( lr= 1e-2 ))
model.summary()
modelpath_h5 = os.path.join(modelpath,
modelname+'-{epoch:03d}-{loss:03f}-{val_loss:03f}.h5')
checkpoint = ModelCheckpoint(modelpath_h5,
verbose=0,
monitor='val_loss',
save_best_only=True,
mode='auto')
return model,checkpoint,modelpath,train,test
def getModel(compile_ = True):
modelpath = MODELPATH
modelname = MODELNAME
if not os.path.exists(modelpath):
os.mkdir(modelpath)
modelpath = os.path.join(modelpath,modelname)
if not os.path.exists(modelpath):
os.mkdir(modelpath)
y_transform = [cutOut([16,80,16,80])]
train,test = getData(BATCH_SIZE,
DIMENSION,CHANNELS,
timeToPred=30,
keep_sequences = True,
y_transform=y_transform)
input_shape = (*DIMENSION,CHANNELS)
model = ZeroInflated_Poisson(input_shape=input_shape)
if compile_ == False:
return model,modelpath,train,test
neg_log_likelihood = lambda x, rv_x: tf.math.reduce_mean(-rv_x.log_prob(x))
model.compile(loss=neg_log_likelihood,
optimizer=Adam( lr= 1e-4 ))
model.summary()
modelpath_h5 = os.path.join(modelpath,
modelname+'-{epoch:03d}-{loss:03f}-{val_loss:03f}.h5')
checkpoint = ModelCheckpoint(modelpath_h5,
verbose=0,
monitor='val_loss',
save_best_only=True,
mode='auto')
return model,checkpoint,modelpath,train,test
def getModel(compile_=True):
modelpath = MODELPATH
modelname = MODELNAME
if not os.path.exists(modelpath):
os.mkdir(modelpath)
modelpath = os.path.join(modelpath, modelname)
if not os.path.exists(modelpath):
os.mkdir(modelpath)
x_transform = [Normalize(0.007742631458799244, 0.015872015890555563)]
train, test = getData(BATCH_SIZE,
DIMENSION,
CHANNELS,
timeToPred=10,
x_transform=x_transform)
input_shape = (*DIMENSION, CHANNELS)
model = ZeroInflated_negativ_Binomial(input_shape=input_shape)
if compile_ == False:
return model, modelpath, train, test
neg_log_likelihood = lambda x, rv_x: tf.math.reduce_mean(-rv_x.log_prob(x))
model.compile(loss=neg_log_likelihood, optimizer=Adam(lr=1e-3))
model.summary()
modelpath_h5 = os.path.join(
modelpath, modelname + '-{epoch:03d}-{loss:03f}-{val_loss:03f}.h5')
checkpoint = ModelCheckpoint(modelpath_h5,
verbose=0,
monitor='val_loss',
save_best_only=True,
mode='auto')
return model, checkpoint, modelpath, train, test
modelpath = MODELPATH
modelname = MODELNAME
if not os.path.exists(modelpath):
os.mkdir(modelpath)
modelpath = os.path.join(modelpath,modelname)
if not os.path.exists(modelpath):
os.mkdir(modelpath)
y_transform = [cutOut([16,80,16,80])]
#x_transform = [Normalize(0.007742631458799244, 0.015872015890555563 )]
train,test = getData(BATCH_SIZE,
DIMENSION,CHANNELS,
timeToPred=30,
keep_sequences = True,
y_transform=y_transform)
neg_log_likelihood = lambda x, rv_x: tf.math.reduce_mean(-rv_x.log_prob(x))
model = CNN_LSTM_Poisson((*DIMENSION,CHANNELS))
model.compile(loss=neg_log_likelihood,
optimizer=Adam( lr= 1e-4 ))
model.summary()
modelpath_h5 = os.path.join(modelpath,
modelname+'-{epoch:03d}-{loss:03f}-{val_loss:03f}.h5')
modelpath = MODELPATH
modelname = MODELNAME
if not os.path.exists(modelpath):
os.mkdir(modelpath)
modelpath = os.path.join(modelpath, modelname)
if not os.path.exists(modelpath):
os.mkdir(modelpath)
x_transform = [Normalize(0.007742631458799244, 0.015872015890555563)]
train, test = getData(BATCH_SIZE,
DIMENSION,
CHANNELS,
timeToPred=30,
x_transform=x_transform)
neg_log_likelihood = lambda x, rv_x: tf.math.reduce_mean(-rv_x.log_prob(x))
model = CNN_LSTM((*DIMENSION, CHANNELS))
model.compile(loss=neg_log_likelihood, optimizer=Adam(lr=1e-4))
model.summary()
modelpath_h5 = os.path.join(
modelpath, modelname + '-{epoch:03d}-{loss:03f}-{val_loss:03f}.h5')
checkpoint = ModelCheckpoint(modelpath_h5,
verbose=0,
monitor='val_loss',
