def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
# If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
if (len(K.int_shape(y_true)) == len(K.int_shape(y_pred))):
y_true = array_ops.squeeze(y_true, [-1])
return K.mean(nn.in_top_k(y_pred, math_ops.cast(y_true, 'int32'), k),
axis=-1)
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
# If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
if (len(K.int_shape(y_true)) == len(K.int_shape(y_pred))):
y_true = array_ops.squeeze(y_true, [-1])
return K.mean(nn.in_top_k(y_pred, math_ops.cast(y_true, 'int32'), k), axis=-1)
def top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(
nn.in_top_k(y_pred, math_ops.argmax(y_true, axis=-1), k), axis=-1)
def _top_k(probabilities, targets):
targets = math_ops.to_int32(targets)
if targets.get_shape().ndims > 1:
targets = array_ops.squeeze(targets, squeeze_dims=[1])
return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets,
k))
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(
nn.in_top_k(y_pred,
math_ops.cast(math_ops.reduce_max(y_true, axis=-1), 'int32'),
k),
axis=-1)
def top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(nn.in_top_k(y_pred, math_ops.argmax(y_true, axis=-1), k),
axis=-1)
def _top_k(probabilities, targets):
targets = math_ops.to_int32(targets)
if targets.get_shape().ndims > 1:
targets = array_ops.squeeze(targets, squeeze_dims=[1])
return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k))
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(nn.in_top_k(
y_pred, math_ops.cast(math_ops.reduce_max(y_true, axis=-1), 'int32'),
k),
axis=-1)
def _top_k(probabilities, targets):
return metric_ops.streaming_mean(
nn.in_top_k(probabilities, math_ops.to_int32(targets), k))
def _top_k(probabilities, targets):
targets = math_ops.cast(targets, dtypes.int32)
if targets.get_shape().ndims > 1:
targets = array_ops.squeeze(targets, axis=[1])
return metrics.mean(nn.in_top_k(probabilities, targets, k))
def _top_k(probabilities, targets):
return metric_ops.streaming_mean(nn.in_top_k(probabilities,
math_ops.to_int32(targets), k))
def _top_k(probabilities, targets):
targets = math_ops.cast(targets, dtypes.int32)
if targets.get_shape().ndims > 1:
targets = array_ops.squeeze(targets, axis=[1])
return metrics.mean(nn.in_top_k(probabilities, targets, k))
def model(a, b):
return nn.in_top_k(a, b, topn)