def __init__(self, model_in, model_out, model_architecture,
sample_fraction):
## This creates an instance of an object of type model
## Of the given inputs model_in, model_out and model_architecture are strings
## sample fractions is a float of value between 0.0-1.0
self.sample_fraction = sample_fraction
self.model_architecture = model_architecture
## Notice how the class variables sample_fraction and model_architecture
## are attributed to their respective inputs
self.model_name = model_in + '_' + model_out + '_' + model_architecture + '_' + str(
sample_fraction)
## model_name is a string combination of all the inputs.
self.trials_file = './trials/' + self.model_name + '.pkl'
self.stats_file = './model_stats/' + self.model_name + '.pkl'
self.plotpairs_file = './plotpairs/' + self.model_name + '.pkl'
## The above class variables are string pathways to respective pickle files needed to construct the model
## These class variables are also dependent on the previously mentioned variable model_name
self.figure_file = './figures/' + self.model_name + '.png'
## figure_file is a string pathway to access the corresponding model image given the model_name
self.model_loc = './models/' + self.model_name
## model_loc is a string pathway showing the location of the model.
self._model = get_model(model_architecture) #ridge,forest,svm,nn(emb,)
## This sets a self._model to the regression typoe used to build the model, model_architercture is a string input
## If-else statement checks if the following model has already been run.
## if it does exsist then it access them and assigns them to respective class variable
## else it sets them to a default value.
if (self.load_hyp() == True):
self.model_stats = pickle.load(open(self.stats_file, "rb"))
[self.plotpairs_cv,
self.plotpairs_test] = pickle.load(open(self.plotpairs_file,
'rb'))
else:
print('No previous trial data, model data or plot data available')
self.model_stats = {
'cv_avg_loss': np.inf,
'cv_std_loss': [],
'test_avg_loss': np.inf,
'test_std_loss': []
}
[self.model_plotpairs_cv,
self.model_plotpairs_test] = [[[], [], []], [[], [], []]]
self.plot_type = None
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
if Train:
if not(os.path.exists('models')):
os.mkdir('models')
if not(os.path.exists('models/'+dataset)):
os.mkdir('models/'+dataset)
for resid_levels in range(2,3): #range(1,4):
print 'training with', resid_levels,'levels'
sess=K.get_session()
model=get_model(dataset,resid_levels,LUT,BINARY,trainable_means)
#model.summary()
#gather all binary dense and binary convolution layers:
binary_layers=[]
for l in model.layers:
if isinstance(l,binary_dense) or isinstance(l,binary_conv):
binary_layers.append(l)
#gather all residual binary activation layers:
resid_bin_layers=[]
for l in model.layers:
if isinstance(l,Residual_sign):
resid_bin_layers.append(l)
lr=0.01
decay=1e-6
def build_architecture(self, model_architecture):
'load architecture class which sets hyp space'
self._model = get_model(model_architecture)
def build_architecture(self, model_architecture):
## This function is used to create a model_architecure model object and make it a protected class variable
## also acts aas a setter function
'load architecture class which sets hyp space'
self._model = get_model(model_architecture)
def step_decay(epoch):
initial_lrate = 0.025
drop = 0.5
epochs_drop = 50.0
lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))
return lrate
if Train:
if not(os.path.exists('models')):
os.mkdir('models')
if not(os.path.exists('models/'+dataset)):
os.mkdir('models/'+dataset)
for resid_levels in range(1):
print 'training with', resid_levels,'levels'
sess=K.get_session()
model=get_model(dataset,resid_levels)
#model.summary()
#gather all binary dense and binary convolution layers:
binary_layers=[]
for l in model.layers:
if isinstance(l,binary_dense) or isinstance(l,binary_conv):
binary_layers.append(l)
#gather all residual binary activation layers:
resid_bin_layers=[]
for l in model.layers:
if isinstance(l,Residual_sign):
resid_bin_layers.append(l)
lr=0.001
#opt = keras.optimizers.Adam(lr=lr,decay=1e-6)#SGD(lr=lr,momentum=0.9,decay=1e-5)