2VhDHddlmZddlmZedddZedddZeddd Zed dd Zed dd ZedddZ edddZ edddZ edddZ edddZ y))backend) keras_exportzkeras.random.normalNcJtjj|||||S)aDraw random samples from a normal (Gaussian) distribution. Args: shape: The shape of the random values to generate. mean: Float, defaults to 0. Mean of the random values to generate. stddev: Float, defaults to 1. Standard deviation of the random values to generate. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `keras.config.floatx()` is used, which defaults to `float32` unless you configured it otherwise (via `keras.config.set_floatx(float_dtype)`). seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value `seed=None` will produce an error, and a `seed` argument must be provided. meanstddevdtypeseed)rrandomnormalshaperrr r s G/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/random/random.pyr r s+6 >>  Du4 ! zkeras.random.categoricalcttj|j}t |dk7rt d|tj j||||S)aDraws samples from a categorical distribution. This function takes as input `logits`, a 2-D input tensor with shape (batch_size, num_classes). Each row of the input represents a categorical distribution, with each column index containing the log-probability for a given class. The function will output a 2-D tensor with shape (batch_size, num_samples), where each row contains samples from the corresponding row in `logits`. Each column index contains an independent samples drawn from the input distribution. Args: logits: 2-D Tensor with shape (batch_size, num_classes). Each row should define a categorical distribution with the unnormalized log-probabilities for all classes. num_samples: Int, the number of independent samples to draw for each row of the input. This will be the second dimension of the output tensor's shape. dtype: Optional dtype of the output tensor. seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. Returns: A 2-D tensor with (batch_size, num_samples). zW`logits` should be a 2-D tensor with shape [batch_size, num_classes]. Received: logits=)r r )listrconvert_to_tensorrlen ValueErrorr categorical)logits num_samplesr r logits_shapes rrr%sqN11&9??@L <A ;;A( D   >> % % 5t & rzkeras.random.uniformc|r$tj|std|dtjj |||||S)aDraw samples from a uniform distribution. The generated values follow a uniform distribution in the range `[minval, maxval)`. The lower bound `minval` is included in the range, while the upper bound `maxval` is excluded. `dtype` must be a floating point type, the default range is `[0, 1)`. Args: shape: The shape of the random values to generate. minval: Float, defaults to 0. Lower bound of the range of random values to generate (inclusive). maxval: Float, defaults to 1. Upper bound of the range of random values to generate (exclusive). dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `keras.config.floatx()` is used, which defaults to `float32` unless you configured it otherwise (via `keras.config.set_floatx(float_dtype)`) seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. zJ`keras.random.uniform` requires a floating point `dtype`. Received: dtype= minvalmaxvalr r )ris_float_dtyperr uniformrrrr r s rr!r!Ws\D W++E2 $gQ (   >> ! ! fV5t " rzkeras.random.randintc|r$tj|std|dtjj |||||S)aDraw random integers from a uniform distribution. The generated values follow a uniform distribution in the range `[minval, maxval)`. The lower bound `minval` is included in the range, while the upper bound `maxval` is excluded. `dtype` must be an integer type. Args: shape: The shape of the random values to generate. minval: Float, defaults to 0. Lower bound of the range of random values to generate (inclusive). maxval: Float, defaults to 1. Upper bound of the range of random values to generate (exclusive). dtype: Optional dtype of the tensor. Only integer types are supported. If not specified, `keras.config.floatx()` is used, which defaults to `float32` unless you configured it otherwise (via `keras.config.set_floatx(float_dtype)`) seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. zD`keras.random.randint` requires an integer `dtype`. Received: dtype=rr)r is_int_dtyperr randintr"s rr%r%s\D W))%0 $gQ (   >> ! ! fV5t " rzkeras.random.truncated_normalcJtjj|||||S)aDraw samples from a truncated normal distribution. The values are drawn from a normal distribution with specified mean and standard deviation, discarding and re-drawing any samples that are more than two standard deviations from the mean. Args: shape: The shape of the random values to generate. mean: Float, defaults to 0. Mean of the random values to generate. stddev: Float, defaults to 1. Standard deviation of the random values to generate. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `keras.config.floatx()` is used, which defaults to `float32` unless you configured it otherwise (via `keras.config.set_floatx(float_dtype)`) seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. r)rr truncated_normalr s rr'r's+> >> * * Du4 + rzkeras.random.dropoutcHtjj||||S)N) noise_shaper )rr dropout)inputsrater)r s rr*r*s' >> ! !+D " rzkeras.random.shufflecFtjj|||S)apShuffle the elements of a tensor uniformly at random along an axis. Args: x: The tensor to be shuffled. axis: An integer specifying the axis along which to shuffle. Defaults to `0`. seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. )axisr )rr shuffle)xr.r s rr/r/s , >> ! !!$T ! ::rzkeras.random.gammacHtjj||||S)acDraw random samples from the Gamma distribution. Args: shape: The shape of the random values to generate. alpha: Float, the parameter of the distribution. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `keras.config.floatx()` is used, which defaults to `float32` unless you configured it otherwise (via `keras.config.set_floatx(float_dtype)`). seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. )alphar r )rr gamma)rr2r r s rr3r3s"2 >>  U%d  KKrzkeras.random.binomialcJtjj|||||S)aDraw samples from a Binomial distribution. The values are drawn from a Binomial distribution with specified trial count and probability of success. Args: shape: The shape of the random values to generate. counts: A number or array of numbers representing the number of trials. It must be broadcastable with `probabilities`. probabilities: A float or array of floats representing the probability of success of an individual event. It must be broadcastable with `counts`. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `keras.config.floatx()` is used, which defaults to `float32` unless you configured it otherwise (via `keras.config.set_floatx(float_dtype)`). seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. )counts probabilitiesr r )rr binomial)rr5r6r r s rr7r7s0@ >> " " #  # rzkeras.random.betacJtjj|||||S)aDraw samples from a Beta distribution. The values are drawn from a Beta distribution parametrized by alpha and beta. Args: shape: The shape of the random values to generate. alpha: Float or an array of floats representing the first parameter alpha. Must be broadcastable with `beta` and `shape`. beta: Float or an array of floats representing the second parameter beta. Must be broadcastable with `alpha` and `shape`. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `keras.config.floatx()` is used, which defaults to `float32` unless you configured it otherwise (via `keras.config.set_floatx(float_dtype)`). seed: Optional Python integer or instance of `keras.random.SeedGenerator`. By default, the `seed` argument is `None`, and an internal global `keras.random.SeedGenerator` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `keras.random.SeedGenerator` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `keras.random.SeedGenerator` is not supported. Therefore, during tracing the default value seed=None will produce an error, and a `seed` argument must be provided. rr2betar r )rr r:r9s rr:r:8s+> >>  5t5t  r)gg?NN)int32N)NN)rN) keras.srcrkeras.src.api_exportrr rr!r%r'r*r/r3r7r:rrr?s-#$%>().*.b$%(&(V$%(&(V-. / F$%& $%;&;0"#L$L6%&%'%P!" # r