op_sparse_categorical_crossentropy: Computes sparse categorical cross-entropy loss.
In rstudio/keras: R Interface to 'Keras'

op_sparse_categorical_crossentropy

R Documentation

Computes sparse categorical cross-entropy loss.

Description

The sparse categorical cross-entropy loss is similar to categorical cross-entropy, but it is used when the target tensor contains integer class labels instead of one-hot encoded vectors. It measures the dissimilarity between the target and output probabilities or logits.

Usage

op_sparse_categorical_crossentropy(
  target,
  output,
  from_logits = FALSE,
  axis = -1L
)

Arguments

`target`	The target tensor representing the true class labels as integers. Its shape should match the shape of the `output` tensor except for the last dimension.
`output`	The output tensor representing the predicted probabilities or logits. Its shape should match the shape of the `target` tensor except for the last dimension.
`from_logits`	(optional) Whether `output` is a tensor of logits or probabilities. Set it to `TRUE` if `output` represents logits; otherwise, set it to `FALSE` if `output` represents probabilities. Defaults to`FALSE`.
`axis`	(optional) The axis along which the sparse categorical cross-entropy is computed. Defaults to `-1`, which corresponds to the last dimension of the tensors.

Value

Integer tensor: The computed sparse categorical cross-entropy loss between target and output.

Examples

target <- op_array(c(0, 1, 2), dtype="int32")
output <- op_array(rbind(c(0.9, 0.05, 0.05),
                        c(0.1, 0.8,  0.1),
                        c(0.2, 0.3,  0.5)))
op_sparse_categorical_crossentropy(target, output)

## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)

Other nn ops:
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op_batch_normalization()
op_binary_crossentropy()
op_categorical_crossentropy()
op_conv()
op_conv_transpose()
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op_hard_sigmoid()
op_hard_silu()
op_leaky_relu()
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op_log_softmax()
op_max_pool()
op_moments()
op_multi_hot()
op_normalize()
op_one_hot()
op_relu()
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op_selu()
op_separable_conv()
op_sigmoid()
op_silu()
op_softmax()
op_softplus()
op_softsign()

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op_binary_crossentropy()
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op_broadcast_to()
op_cast()
op_categorical_crossentropy()
op_ceil()
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op_clip()
op_concatenate()
op_cond()
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op_conv()
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op_convert_to_numpy()
op_convert_to_tensor()
op_copy()
op_correlate()
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op_count_nonzero()
op_cross()
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op_depthwise_conv()
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op_equal()
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op_exp()
op_expand_dims()
op_expm1()
op_extract_sequences()
op_eye()
op_fft()
op_fft2()
op_flip()
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op_fori_loop()
op_full()
op_full_like()
op_gelu()
op_get_item()
op_greater()
op_greater_equal()
op_hard_sigmoid()
op_hard_silu()
op_hstack()
op_identity()
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op_image_crop()
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op_image_pad()
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op_isclose()
op_isfinite()
op_isinf()
op_isnan()
op_istft()
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op_less()
op_less_equal()
op_linspace()
op_log()
op_log10()
op_log1p()
op_log2()
op_log_sigmoid()
op_log_softmax()
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op_moveaxis()
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op_multiply()
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op_negative()
op_nonzero()
op_norm()
op_normalize()
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op_ones()
op_ones_like()
op_outer()
op_pad()
op_power()
op_prod()
op_qr()
op_quantile()
op_ravel()
op_real()
op_reciprocal()
op_relu()
op_relu6()
op_repeat()
op_reshape()
op_rfft()
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op_round()
op_rsqrt()
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op_scatter_update()
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op_separable_conv()
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op_sign()
op_silu()
op_sin()
op_sinh()
op_size()
op_slice()
op_slice_update()
op_softmax()
op_softplus()
op_softsign()
op_solve()
op_solve_triangular()
op_sort()
op_split()
op_sqrt()
op_square()
op_squeeze()
op_stack()
op_std()
op_stft()
op_stop_gradient()
op_subtract()
op_sum()
op_svd()
op_swapaxes()
op_take()
op_take_along_axis()
op_tan()
op_tanh()
op_tensordot()
op_tile()
op_top_k()
op_trace()
op_transpose()
op_tri()
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op_triu()
op_unstack()
op_var()
op_vdot()
op_vectorize()
op_vectorized_map()
op_vstack()
op_where()
op_while_loop()
op_zeros()
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