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>>> loss = tf.keras.losses.poisson(y_true, y_pred) |
>>> assert loss.shape == (2,) |
>>> y_pred = y_pred + 1e-7 |
>>> assert np.allclose( |
... loss.numpy(), np.mean(y_pred - y_true * np.log(y_pred), axis=-1), |
... atol=1e-5) |
Arguments |
y_true: Ground truth values. shape = [batch_size, d0, .. dN]. |
y_pred: The predicted values. shape = [batch_size, d0, .. dN]. |
Returns |
Poisson loss value. shape = [batch_size, d0, .. dN-1]. |
Raises |
InvalidArgumentError: If y_true and y_pred have incompatible shapes. |
KLDivergence class |
tf.keras.losses.KLDivergence(reduction="auto", name="kl_divergence") |
Computes Kullback-Leibler divergence loss between y_true and y_pred. |
loss = y_true * log(y_true / y_pred) |
See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence |
Standalone usage: |
>>> y_true = [[0, 1], [0, 0]] |
>>> y_pred = [[0.6, 0.4], [0.4, 0.6]] |
>>> # Using 'auto'/'sum_over_batch_size' reduction type. |
>>> kl = tf.keras.losses.KLDivergence() |
>>> kl(y_true, y_pred).numpy() |
0.458 |
>>> # Calling with 'sample_weight'. |
>>> kl(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() |
0.366 |
>>> # Using 'sum' reduction type. |
>>> kl = tf.keras.losses.KLDivergence( |
... reduction=tf.keras.losses.Reduction.SUM) |
>>> kl(y_true, y_pred).numpy() |
0.916 |
>>> # Using 'none' reduction type. |
>>> kl = tf.keras.losses.KLDivergence( |
... reduction=tf.keras.losses.Reduction.NONE) |
>>> kl(y_true, y_pred).numpy() |
array([0.916, -3.08e-06], dtype=float32) |
Usage with the compile() API: |
model.compile(optimizer='sgd', loss=tf.keras.losses.KLDivergence()) |
kl_divergence function |
tf.keras.losses.kl_divergence(y_true, y_pred) |
Computes Kullback-Leibler divergence loss between y_true and y_pred. |
loss = y_true * log(y_true / y_pred) |
See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence |
Standalone usage: |
>>> y_true = np.random.randint(0, 2, size=(2, 3)).astype(np.float64) |
>>> y_pred = np.random.random(size=(2, 3)) |
>>> loss = tf.keras.losses.kullback_leibler_divergence(y_true, y_pred) |
>>> assert loss.shape == (2,) |
>>> y_true = tf.keras.backend.clip(y_true, 1e-7, 1) |
>>> y_pred = tf.keras.backend.clip(y_pred, 1e-7, 1) |
>>> assert np.array_equal( |
... loss.numpy(), np.sum(y_true * np.log(y_true / y_pred), axis=-1)) |
Arguments |
y_true: Tensor of true targets. |
y_pred: Tensor of predicted targets. |
Returns |
A Tensor with loss. |
Raises |
TypeError: If y_true cannot be cast to the y_pred.dtype. |
Backend utilities |
clear_session function |
tf.keras.backend.clear_session() |
Resets all state generated by Keras. |
Keras manages a global state, which it uses to implement the Functional model-building API and to uniquify autogenerated layer names. |
If you are creating many models in a loop, this global state will consume an increasing amount of memory over time, and you may want to clear it. Calling clear_session() releases the global state: this helps avoid clutter from old models and layers, especially when memory is limited. |
Example 1: calling clear_session() when creating models in a loop |