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from_logits=False, reduction="auto", name="sparse_categorical_crossentropy"
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)
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Computes the crossentropy loss between the labels and predictions.
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Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using one-hot representation, please use CategoricalCrossentropy loss. There should be # classes floating point values per feature for y_pred and a single floating point value per feature for y_true.
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In the snippet below, there is a single floating point value per example for y_true and # classes floating pointing values per example for y_pred. The shape of y_true is [batch_size] and the shape of y_pred is [batch_size, num_classes].
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Standalone usage:
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>>> y_true = [1, 2]
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>>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
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>>> # Using 'auto'/'sum_over_batch_size' reduction type.
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>>> scce = tf.keras.losses.SparseCategoricalCrossentropy()
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>>> scce(y_true, y_pred).numpy()
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1.177
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>>> # Calling with 'sample_weight'.
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>>> scce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
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0.814
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>>> # Using 'sum' reduction type.
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>>> scce = tf.keras.losses.SparseCategoricalCrossentropy(
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... reduction=tf.keras.losses.Reduction.SUM)
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>>> scce(y_true, y_pred).numpy()
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2.354
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>>> # Using 'none' reduction type.
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>>> scce = tf.keras.losses.SparseCategoricalCrossentropy(
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... reduction=tf.keras.losses.Reduction.NONE)
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>>> scce(y_true, y_pred).numpy()
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array([0.0513, 2.303], dtype=float32)
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Usage with the compile() API:
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model.compile(optimizer='sgd',
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loss=tf.keras.losses.SparseCategoricalCrossentropy())
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Poisson class
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tf.keras.losses.Poisson(reduction="auto", name="poisson")
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Computes the Poisson loss between y_true and y_pred.
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loss = y_pred - y_true * log(y_pred)
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Standalone usage:
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>>> y_true = [[0., 1.], [0., 0.]]
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>>> y_pred = [[1., 1.], [0., 0.]]
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>>> # Using 'auto'/'sum_over_batch_size' reduction type.
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>>> p = tf.keras.losses.Poisson()
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>>> p(y_true, y_pred).numpy()
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0.5
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>>> # Calling with 'sample_weight'.
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>>> p(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
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0.4
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>>> # Using 'sum' reduction type.
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>>> p = tf.keras.losses.Poisson(
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... reduction=tf.keras.losses.Reduction.SUM)
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>>> p(y_true, y_pred).numpy()
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0.999
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>>> # Using 'none' reduction type.
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>>> p = tf.keras.losses.Poisson(
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... reduction=tf.keras.losses.Reduction.NONE)
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>>> p(y_true, y_pred).numpy()
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array([0.999, 0.], dtype=float32)
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Usage with the compile() API:
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model.compile(optimizer='sgd', loss=tf.keras.losses.Poisson())
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binary_crossentropy function
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tf.keras.losses.binary_crossentropy(
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y_true, y_pred, from_logits=False, label_smoothing=0
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)
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Computes the binary crossentropy loss.
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Standalone usage:
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>>> y_true = [[0, 1], [0, 0]]
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>>> y_pred = [[0.6, 0.4], [0.4, 0.6]]
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>>> loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
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>>> assert loss.shape == (2,)
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>>> loss.numpy()
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array([0.916 , 0.714], dtype=float32)
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Arguments
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y_true: Ground truth values. shape = [batch_size, d0, .. dN].
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y_pred: The predicted values. shape = [batch_size, d0, .. dN].
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from_logits: Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
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