def ensemble_clip_predictions(test,
rbp_name,
models,
input_shape,
output_shape,
best_path,
ss_type,
use_scope=True):
predictions = []
for model in models:
# load model
genome_model = import_model(model)
model_layers, optimization = genome_model(input_shape, output_shape)
# build neural network class
nnmodel = nn.NeuralNet(seed=247)
nnmodel.build_layers(model_layers, optimization, use_scope=use_scope)
file_path = os.path.join(best_path, model, ss_type, rbp_name)
nntrainer = nn.NeuralTrainer(nnmodel, save='best', file_path=file_path)
# initialize session
sess = utils.initialize_session(nnmodel.placeholders)
# load best model
nntrainer.set_best_parameters(sess, verbose=0)
predictions.append(nntrainer.get_activations(sess, test))
predictions = np.hstack(predictions)
ensemble_predictions = np.mean(predictions, axis=1)
return ensemble_predictions, predictions
def stochastic_backprop(X,
layer='output',
class_index=None,
params=None,
num_average=400,
threshold=12.0):
tf.reset_default_graph()
if 'use_scope' not in params:
params['use_scope'] = True
# build new graph
model_layers, optimization = params['genome_model'](params['input_shape'],
params['output_shape'])
nnmodel = nn.NeuralNet()
nnmodel.build_layers(model_layers,
optimization,
use_scope=params['use_scope'])
nntrainer = nn.NeuralTrainer(nnmodel,
save='best',
filepath=params['model_path'])
# setup session and restore optimal parameters
sess = utils.initialize_session(nnmodel.placeholders)
nntrainer.set_best_parameters(sess, params['model_path'], verbose=0)
# stochastic backprop saliency
if layer == 'output':
layer = list(nnmodel.network.keys())[-2]
saliency, counts = nntrainer.get_stochastic_saliency(
sess,
X,
nnmodel.network[layer],
class_index=class_index,
num_average=num_average,
threshold=threshold)
else:
data = {'inputs': X}
layer_activations = nntrainer.get_activations(sess, data, layer)
max_activations = np.squeeze(np.max(layer_activations, axis=1))
active_indices = np.where(max_activations > 0)[0]
active_indices = active_indices[np.argsort(
max_activations[active_indices])[::-1]]
saliency = []
count = []
for neuron_index in active_indices:
val, counts = nntrainer.get_stochastic_saliency(
sess,
X,
nnmodel.network[layer],
class_index=neuron_index,
num_average=num_average,
threshold=threshold)
saliency.append(val)
counts.append(count)
sess.close()
tf.reset_default_graph()
return np.vstack(saliency), np.array(counts)
def guided_backprop(X,
layer='output',
class_index=None,
params=None,
batch_size=128):
tf.reset_default_graph()
if 'use_scope' not in params:
params['use_scope'] = True
# build new graph
#g = tf.get_default_graph()
#with g.gradient_override_map({'Relu': 'GuidedRelu'}):
model_layers, optimization = params['genome_model'](params['input_shape'],
params['output_shape'])
nnmodel = nn.NeuralNet()
nnmodel.build_layers(model_layers,
optimization,
method='guided',
use_scope=params['use_scope'])
nntrainer = nn.NeuralTrainer(nnmodel,
save='best',
filepath=params['model_path'])
# setup session and restore optimal parameters
sess = utils.initialize_session(nnmodel.placeholders)
nntrainer.set_best_parameters(sess, params['model_path'], verbose=0)
# backprop saliency
if layer == 'output':
layer = list(nnmodel.network.keys())[-2]
saliency = nntrainer.get_saliency(sess,
X,
nnmodel.network[layer],
class_index=class_index,
batch_size=batch_size)
else:
data = {nnmodel.placeholders['inputs']: X}
layer_activations = nntrainer.get_activations(sess, data, layer)
max_activations = np.squeeze(np.max(layer_activations, axis=1))
active_indices = np.where(max_activations > 0)[0]
active_indices = active_indices[np.argsort(
max_activations[active_indices])[::-1]]
saliency = []
for neuron_index in active_indices:
val = nntrainer.get_saliency(sess,
X,
nnmodel.network[layer][:, :, :,
neuron_index],
class_index=None,
batch_size=batch_size)
saliency.append(val)
sess.close()
tf.reset_default_graph()
return saliency
inputs = np.vstack([mean_saliency, background])
targets = np.vstack([np.ones((num_saliency,1)), np.zeros((num_saliency,1))])
shuffle = np.random.permutation(inputs.shape[0])
new_train = {'inputs': inputs[shuffle], 'targets': targets[shuffle]}
# load classifier model
model_layers, optimization = classifier_model(input_shape, output_shape, num_filters)
# build neural network class
nnmodel = nn.NeuralNet(seed=247)
nnmodel.build_layers(model_layers, optimization)
nnmodel.inspect_layers()
# compile neural trainer
file_path = os.path.join(params_path, 'saliency_classifier_'+str(num_saliency))
nntrainer = nn.NeuralTrainer(nnmodel, save='best', file_path=file_path)
# initialize session
sess = utils.initialize_session(nnmodel.placeholders)
# fit data
data = {'train': new_train}
fit.train_minibatch(sess, nntrainer, data, batch_size=32, num_epochs=500,
patience=20, verbose=2, shuffle=True, save_all=False)
# plot mean activations for each filter
data={'inputs': mean_saliency}
fmaps = nntrainer.get_activations(sess, data, layer='conv1d_0_active')
mean_fmap = np.squeeze(np.mean(fmaps, axis=0))
fig = plt.figure()
input_shape = list(train['inputs'].shape) input_shape[0] = None output_shape = train['targets'].shape # load model genome_model = helper.import_model(model) model_layers, optimization = genome_model(input_shape, output_shape) # build neural network class nnmodel = nn.NeuralNet(seed=247) nnmodel.build_layers(model_layers, optimization) # compile neural trainer file_path = os.path.join(model_path, rbp_name) nntrainer = nn.NeuralTrainer(nnmodel, save='best', file_path=file_path) # initialize session sess = utils.initialize_session(nnmodel.placeholders) # load best parameters nntrainer.set_best_parameters(sess) # get predictions predictions = nntrainer.get_activations(sess, train, layer='output') max_index = np.argsort(predictions[:,0])[::-1] # close tensorflow session sess.close() # directories to store classifier model and motifs
