Python Apply Exemples

Exemple #1

def subtract(x, biases):
    if isinstance(x, expr.Constant) and isinstance(biases, expr.Constant):
        return expr.Constant(subtract_fn.forward([x.value, biases.value])[0])
    else:
        return expr.Apply(subtract_fn, [x, biases])

Exemple #2

def softmax(x):
    if isinstance(x, expr.Constant):
        return expr.Constant(softmax_fn.forward([x.value])[0])
    else:
        return expr.Apply(softmax_fn, [x])

Exemple #3

Fichier : reshape.py Projet : danielwaterworth/cognite

def reshape(values, new_shape):
    if isinstance(values, expr.Constant):
        return expr.Constant(Reshape(new_shape).forward([values.value]))
    else:
        return expr.Apply(Reshape(new_shape), [values])

Exemple #4

Fichier : mean.py Projet : danielwaterworth/cognite

def mean(values, dims):
    if isinstance(values, expr.Constant):
        return expr.Constant(Mean(dims).forward(values.value))
    else:
        return expr.Apply(Mean(dims), [values])

Exemple #5

Fichier : squared_difference.py Projet : danielwaterworth/cognite

def squared_difference(x, weights):
    if isinstance(x, expr.Constant) and isinstance(weights, expr.Constant):
        return expr.Constant(squared_difference_fn.forward([x.value, weights.value])[0])
    else:
        return expr.Apply(squared_difference_fn, [x, weights])

Exemple #6

def sigmoid_cross_entropy(x, labels):
    if isinstance(x, expr.Constant) and isinstance(labels, expr.Constant):
        return expr.Constant(
            sigmoid_cross_entropy_fn.forward([x.value, labels.value])[0])
    else:
        return expr.Apply(sigmoid_cross_entropy_fn, [x, labels])

Exemple #7

Fichier : upsample.py Projet : danielwaterworth/cognite

def upsample(values, scale):
    if isinstance(values, expr.Constant):
        return expr.Constant(Upsample(scale).forward(values.value))
    else:
        return expr.Apply(Upsample(scale), [values])

Exemple #8

def add_biases(x, biases):
    if isinstance(x, expr.Constant) and isinstance(biases, expr.Constant):
        return expr.Constant(add_biases_fn.forward([x.value, biases.value])[0])
    else:
        return expr.Apply(add_biases_fn, [x, biases])

Exemple #9

Fichier : linear.py Projet : danielwaterworth/cognite

def linear(x, weights):
    if isinstance(x, expr.Constant) and isinstance(weights, expr.Constant):
        return expr.Constant(linear_fn.forward([x.value, weights.value])[0])
    else:
        return expr.Apply(linear_fn, [x, weights])

Exemple #10

def sigmoid(x):
    if isinstance(x, expr.Constant):
        return expr.Constant(sigmoid_fn.forward([x.value])[0])
    else:
        return expr.Apply(sigmoid_fn, [x])

Exemple #11

def concat(args, axis):
    if all(lambda x: isinstance(expr.Constant), args):
        return expr.Constant(
            Concat(axis).forward(map(lambda x: x.value, args)))
    else:
        return expr.Apply(Concat(axis), [args])

Exemple #12

Fichier : broadcast.py Projet : danielwaterworth/cognite

def broadcast(x, shape=None):
    if isinstance(x, expr.Constant):
        return expr.Constant(Broadcast(shape).forward([x.value])[0])
    else:
        return expr.Apply(Broadcast(shape), [x])