def iris_model(x_train, y_train, x_val, y_val, params):
model = Sequential()
model.add(
Dense(params['first_neuron'],
input_dim=x_train.shape[1],
activation='relu'))
model.add(Dropout(params['dropout']))
model.add(Dense(y_train.shape[1],
activation=params['last_activation']))
model.compile(optimizer=params['optimizer'](
lr=lr_normalizer(params['lr'], params['optimizer'])),
loss=params['loss'],
metrics=['acc'])
out = model.fit(x_train,
y_train,
batch_size=params['batch_size'],
epochs=params['epochs'],
verbose=0,
validation_data=[x_val, y_val])
return out, model
def create_model(params):
# La capa LSTM espera una entrada 3D tal que [samples, timesteps, features]
print ('Creando el modelo.')
model = Sequential()
model.add(LSTM(params['first_layer'], activation='tanh', recurrent_activation='sigmoid', recurrent_dropout=0, unroll=False, use_bias=True))
model.add(Dropout(params['dropout']))
model.add(Dense(1, activation=params['last_activation']))
print ('Compilando el modelo.')
model.compile(loss=params['losses'],
optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])),
metrics=['acc'])
return model
def model(x_train, y_train, x_val, y_val, params):
rcnn = RCNN(ImageData=self.ImageData,
loss=self.params['loss'],
opt=self.params['opt'],
lr=lr_normalizer(self.params['lr'],
self.params['opt']),
seed=self.seed,
verbose=0)
return rcnn.train(epochs=self.params['epochs'],
batch_size=self.params['batch_size'],
split_size=self.params['split_size'],
checkpoint_path=None,
early_stopping=False,
verbose=0)
def talos_model(sub_train_images, y_train, sub_val_images, y_val, params):
print(f"parameters: {params}")
print(f"y_train.shape: {y_train.shape}")
#input_tensor = Input(shape=(224, 224, 3)) # this assumes K.image_data_format() == 'channels_last'
base_model = DenseNet121(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=5,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=True)
mc = ModelCheckpoint('best_model.h5',
monitor='val_loss',
mode='max',
verbose=1,
save_best_only=True)
for layer in base_model.layers:
layer.trainable = True
model.compile(optimizer=params['optimizer'](
lr=lr_normalizer(params['lr'], params['optimizer'])),
loss=loss_fx,
metrics=metrics,
class_weight=class_weight)
out = model.fit_generator(
imageLoader(sub_train_images, y_train, params['batch_size']),
steps_per_epoch=sub_train_images.shape[0] // params['batch_size'],
epochs=20,
validation_data=imageLoader(sub_val_images, y_val,
params['batch_size']),
validation_steps=sub_val_images.shape[0] // params['batch_size'],
callbacks=[es, mc],
verbose=2)
#print(f"out:{out.history.keys()}")
return out, model
def best_model_train():
from keras.optimizers import Adam
# La capa LSTM espera una entrada 3D tal que [samples, timesteps, features]
print ('Creando el modelo.')
model = Sequential()
model.add(LSTM(150, activation='tanh', recurrent_activation='sigmoid', recurrent_dropout=0, unroll=False, use_bias=True, input_shape=(250,136)))
model.add(Dropout(0.35))
model.add(Dense(1, activation='sigmoid'))
print ('Compilando el modelo.')
model.compile(loss='binary_crossentropy',
optimizer=Adam(lr=lr_normalizer(6.04, Adam)),
metrics=['acc'])
data,labels = data_treatment(Path('./landmarks'))
x_train, x_val, y_train, y_val = train_test_split(data, labels, test_size=0.2)
return model.fit(x_train, y_train,
epochs=100,
batch_size=400,
validation_data=(x_val, y_val),
verbose=1, shuffle=False)
def create_model(trainX, trainY, testX, testY, params):
model = Sequential([
Dense(params['first_neuron'],
input_shape=(len(trainX[1]), ),
activation='relu'),
Dense(1)
])
model.compile(optimizer=params['optimizer'](
lr=lr_normalizer(params['lr'], params['optimizer'])),
loss=['mean_squared_error'],
metrics=['mean_squared_error'])
model_out = model.fit(trainX,
trainY,
validation_data=[testX, testY],
batch_size=params['batch_size'],
callbacks=[keras.callbacks.History()],
epochs=params['epochs'],
verbose=0)
return model_out, model
def the_network(X_train, y_train, X_test, y_test, params):
n_network = Sequential()
n_network.add(
Dense(params['first_neuron'],
input_dim=X_train.shape[1],
activation='linear'))
n_network.add(BatchNormalization())
n_network.add(
Dense(params['second_layer'], activation=params['activation']))
n_network.add(Dropout(params['dropout']))
n_network.add(Dense(1, activation=params['activation']))
# Compiling the network
n_network.compile(optimizer=params['optimizer'](
lr=lr_normalizer(params['lr'], params['optimizer'])),
loss=params['loss_func'],
metrics=['mse'])
# Adding callback - early stopping
cb = EarlyStopping(monitor='mse', patience=params['patience'])
# Fitting the network and creating fitting plots
history = n_network.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=params['epochs'],
verbose=0,
batch_size=params['batch_size'],
callbacks=[cb])
return history, n_network
plot_loss(history.history['loss'], history.history['val_loss'])
analyze_object.plot_corr(metric='loss', exclude=['val_loss', 'mse', 'val_mse'])
# Run the neural network on best parameters - selected manually - to be automated
n_network = Sequential()
n_network.add(Dense(32, input_dim=X_train.shape[1], activation='linear'))
n_network.add(BatchNormalization())
n_network.add(Dense(32, activation='relu'))
n_network.add(Dropout(0.25))
n_network.add(Dense(1, activation='relu'))
# Compiling the network
n_network.compile(optimizer=Adam(lr=lr_normalizer(0.1, Adam)),
loss='mse',
metrics=['mse'])
# Adding callback - early stopping
cb = EarlyStopping(monitor='mse', patience=75)
# Fitting the network and creating fitting plots
history = n_network.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=1000,
verbose=1,
batch_size=512,
def model(x_train, y_train, x_val, y_val, p):
#Initialise the model
model = keras.models.Sequential()
#Grab the optimised parameters
nPercent = p["nPercent"]
nCount = nPercent * 50
nShrink = p["nShrink"]
maxLayer = p["maxLayer"]
minNeuron = p["minNeuron"]
firstactivation = p["first_activation"]
hiddenlayeractivation = p["hidden_activation"]
lastactivation = p["last_activation"]
dropout = p["dropOut"]
bSize = p["batchSize"]
epochs = p["epochs"]
#************************** Initialise the optimiser
opt = p["optimiser"](
lr=lr_normalizer(
p["lr"],
p["optimiser"]
)
)
#************************** Grab the loss function
loss = p["loss"]
#********************* Get the regulariser with its expected factor
kernel_regulariser = p["kernel_reg"]
if kernel_regulariser == L1_reg:
regulariser = kernel_regulariser(l1=p["alpha_l1"])
elif kernel_regulariser == L2_reg:
regulariser = kernel_regulariser(l2=p["alpha_l2"])
elif kernel_regulariser == L1L2_reg:
regulariser = kernel_regulariser(l1=p["alpha_l1"],l2=p["alpha_l2"])
#Start the loop
while nCount > minNeuron and layer < maxLayer:
#************************* The first (0th) hidden layer needs an input input_dim(neuronCount)
if layer == 0:
model.add(
keras.layers.Dense(
nCount,
name="Input",
input_dim = x_train.shape[1],
activation = firstactivation,
use_bias= True,
kernel_initializer="he_uniform",
kernel_regularizer= regulariser
)
)
else:
model.add(
keras.layers.Dense(
nCount,
name = "Layer_%s"%(layer + 1)
activation=hiddenlayeractivation,
kernel_initializer ="he_uniform",
use_bias=True,
kernel_regularizer = regulariser
)
)
layer += 1
#*************************** Add dropout after each hidden layer
model.add(backend
Dropout(
dropout,seed=constant
)
)
#*************************** Shrink neuron count for each layer
nCount = nCount * nShrink
#*************************** Output layer
model.add(
keras.layers.Dense(
y_train.shape[1]
name="Output",
activation = lastactivation,
kernel_regularizer = regulariser
)
)