def _class_log_loss(probabilities, targets, weights=None):
return metric_ops.streaming_mean(losses.log_loss(
probabilities,
_squeeze_and_onehot(targets,
array_ops.shape(probabilities)[1])),
weights=weights)
def _class_log_loss(probabilities, targets, weights=None):
return metric_ops.streaming_mean(
losses.log_loss(probabilities,
_squeeze_and_onehot(targets,
array_ops.shape(probabilities)[1])),
weights=weights)
# softmax output. Gives global meaning to both the perceptrons combined
# output is [0.26,0.73], [0.26,0.73].
# [e^6/(e^6+e^7),e^7/(e^6 + e^7) ] = [1/1+e, 1/1+e^-1] = [0.26,0.73]
# [e^12/(e^12+e^13),e^13/(e^12 + e^13) ] = [1/1+e, 1/1+e^-1] = [0.26,0.73]
# Note how the result is very different from that obtained with sigmoid activation function.
nnout6 = layers.fully_connected(
inputs=features,
weights_initializer=tf.constant_initializer([[1.0, 1.0], [2.0, 2.0]]),
biases_initializer=tf.constant_initializer([1.0, 2.0]),
num_outputs=2,
activation_fn=tf.nn.softmax)
session = tf.Session()
session.run(tf.initialize_all_variables())
session.run(nnout6)
outputs = tf.constant([0, 1, 0, 1])
targets = tf.constant([1, 1, 1, 0])
sq_loss1 = losses.mean_squared_error(outputs, targets)
log_loss1 = losses.log_loss(outputs, targets)
outputs2 = tf.constant([[100.0, -100.0, -100.0], [-100.0, 100.0, -100.0],
[-100.0, -100.0, 100.0]])
targets2 = tf.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
sq_loss2 = losses.mean_squared_error(outputs2, targets2)
session = tf.Session()
session.run(tf.initialize_all_variables())
session.run(sq_loss1) # 0.75 = [(0-1)^2 + (1-1)^2 + (0-1)^2 + (1-0)^2] / 4
session.run(sq_loss2) # 10067 = (6*100^2 + 3*101^2)/9
session.run(log_loss1) # sigma(-y_i*log(y_i))
def _log_loss(probabilities, targets):
# targets doesn't have a shape coming in, log_loss isn't too happy about it.
targets = array_ops.reshape(targets, array_ops.shape(probabilities))
return metric_ops.streaming_mean(losses.log_loss(probabilities, targets))
def _log_loss(probabilities, targets): # targets doesn't have a shape coming in, log_loss isn't too happy about it. targets = array_ops.reshape(targets, array_ops.shape(probabilities)) return metric_ops.streaming_mean(losses.log_loss(probabilities, targets))
