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Use this cross-entropy loss for binary (0 or 1) classification applications. The loss function requires the following inputs:
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y_true (true label): This is either 0 or 1.
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y_pred (predicted value): This is the model's prediction, i.e, a single floating-point value which either represents a logit, (i.e, value in [-inf, inf] when from_logits=True) or a probability (i.e, value in [0., 1.] when from_logits=False).
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Recommended Usage: (set from_logits=True)
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With tf.keras API:
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model.compile(
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loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
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....
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)
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As a standalone function:
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>>> # Example 1: (batch_size = 1, number of samples = 4)
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>>> y_true = [0, 1, 0, 0]
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>>> y_pred = [-18.6, 0.51, 2.94, -12.8]
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>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
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>>> bce(y_true, y_pred).numpy()
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0.865
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>>> # Example 2: (batch_size = 2, number of samples = 4)
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>>> y_true = [[0, 1], [0, 0]]
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>>> y_pred = [[-18.6, 0.51], [2.94, -12.8]]
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>>> # Using default 'auto'/'sum_over_batch_size' reduction type.
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>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
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>>> bce(y_true, y_pred).numpy()
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0.865
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>>> # Using 'sample_weight' attribute
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>>> bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
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0.243
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>>> # Using 'sum' reduction` type.
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>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
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... reduction=tf.keras.losses.Reduction.SUM)
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>>> bce(y_true, y_pred).numpy()
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1.730
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>>> # Using 'none' reduction type.
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>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
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... reduction=tf.keras.losses.Reduction.NONE)
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>>> bce(y_true, y_pred).numpy()
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array([0.235, 1.496], dtype=float32)
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Default Usage: (set from_logits=False)
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>>> # Make the following updates to the above "Recommended Usage" section
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>>> # 1. Set `from_logits=False`
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>>> tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False')
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>>> # 2. Update `y_pred` to use probabilities instead of logits
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>>> y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]]
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CategoricalCrossentropy class
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tf.keras.losses.CategoricalCrossentropy(
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from_logits=False,
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label_smoothing=0,
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reduction="auto",
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name="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 in a one_hot representation. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss. There should be # classes floating point values per feature.
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In the snippet below, there is # classes floating pointing values per example. The shape of both y_pred and y_true are [batch_size, num_classes].
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Standalone usage:
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>>> y_true = [[0, 1, 0], [0, 0, 1]]
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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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>>> cce = tf.keras.losses.CategoricalCrossentropy()
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>>> cce(y_true, y_pred).numpy()
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1.177
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>>> # Calling with 'sample_weight'.
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>>> cce(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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>>> cce = tf.keras.losses.CategoricalCrossentropy(
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... reduction=tf.keras.losses.Reduction.SUM)
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>>> cce(y_true, y_pred).numpy()
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2.354
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>>> # Using 'none' reduction type.
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>>> cce = tf.keras.losses.CategoricalCrossentropy(
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... reduction=tf.keras.losses.Reduction.NONE)
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>>> cce(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', loss=tf.keras.losses.CategoricalCrossentropy())
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SparseCategoricalCrossentropy class
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tf.keras.losses.SparseCategoricalCrossentropy(
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