calibration.py
def build_model(frame_w, frame_h, output_dim,
                batch_size=BATCH_SIZE, num_hidden_units=NUM_HIDDEN_UNITS, input_var=None):
    """Create a symbolic representation of the neural network with
    input_dim input nodes
    output_dim output nodes
    num_hidden_units per hidden layer
    input_var symbolic input variable

    return: A theano expression representing the network
    """
    l_in = lasagne.layers.InputLayer(shape=(None, 1, frame_h,frame_w), input_var=input_var)
    l_hidden1 = lasagne.layers.DenseLayer(
        l_in,
        num_units=num_hidden_units,
        nonlinearity=lasagne.nonlinearities.rectify,
    )
    l_hidden2 = lasagne.layers.DenseLayer(
        l_hidden1,
        num_units=num_hidden_units,
        nonlinearity=lasagne.nonlinearities.rectify,
    )
    l_out = lasagne.layers.DenseLayer(
        l_hidden2,
        num_units=output_dim,
        nonlinearity=lasagne.nonlinearities.linear,
        b=None,
    )
    return l_out
Create a symbolic representation of the neural network with
    input_dim input nodes
    output_dim output nodes
    num_hidden_units per hidden layer
    input_var symbolic input variable

    return: A theano expression representing the network
    
Create a symbolic representation of the neural network with
input_dim input nodes
output_dim output nodes
num_hidden_units per hidden layer
input_var symbolic input variable

A theano expression representing the network
Create a symbolic representation of the neural network with input_dim input nodes output_dim output nodes num_hidden_units per hidden layer input_var symbolic input variable A theano expression representing the network
def build_model(frame_w, frame_h, output_dim,
                batch_size=BATCH_SIZE, num_hidden_units=NUM_HIDDEN_UNITS, input_var=None):
    l_in = lasagne.layers.InputLayer(shape=(None, 1, frame_h,frame_w), input_var=input_var)
    l_hidden1 = lasagne.layers.DenseLayer(
        l_in,
        num_units=num_hidden_units,
        nonlinearity=lasagne.nonlinearities.rectify,
    )
    l_hidden2 = lasagne.layers.DenseLayer(
        l_hidden1,
        num_units=num_hidden_units,
        nonlinearity=lasagne.nonlinearities.rectify,
    )
    l_out = lasagne.layers.DenseLayer(
        l_hidden2,
        num_units=output_dim,
        nonlinearity=lasagne.nonlinearities.linear,
        b=None,
    )
    return l_out
def build_model ( frame_w , frame_h , output_dim , batch_size = BATCH_SIZE , num_hidden_units = NUM_HIDDEN_UNITS , input_var = None ) : l_in = lasagne . layers . InputLayer ( shape = ( None , 1 , frame_h , frame_w ) , input_var = input_var ) l_hidden1 = lasagne . layers . DenseLayer ( l_in , num_units = num_hidden_units , nonlinearity = lasagne . nonlinearities . rectify , ) l_hidden2 = lasagne . layers . DenseLayer ( l_hidden1 , num_units = num_hidden_units , nonlinearity = lasagne . nonlinearities . rectify , ) l_out = lasagne . layers . DenseLayer ( l_hidden2 , num_units = output_dim , nonlinearity = lasagne . nonlinearities . linear , b = None , ) return l_out
Create a symbolic representation of the neural network with
input_dim input nodes
output_dim output nodes
num_hidden_units per hidden layer
input_var symbolic input variable
Create a symbolic representation of the neural network with input_dim input nodes output_dim output nodes num_hidden_units per hidden layer input_var symbolic input variable
"""Create a symbolic representation of the neural network with
    input_dim input nodes
    output_dim output nodes
    num_hidden_units per hidden layer
    input_var symbolic input variable

    return: A theano expression representing the network
    """
frame_w
frame_h
output_dim
batch_size
num_hidden_units
input_var