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Feb 21, 2023, 7:05:46 PM2/21/23

to TensorFlow Probability

Please can somebody help me understand the reason for the error that I am getting:

@tf.function

def utilities(x, betas, errors):

"""

`x * betas + errors` with broadcasting.

"""

x = tf.cast(x, dtype=tf.float32)

return tf.transpose(x) * betas + errors

k = 10

sigma_beta = 5.

sigma_error = 1.

def alt_pooled_model(X_train):

return tfd.JointDistributionSequential([

tfd.HalfCauchy(loc=0., scale=sigma_beta, name="sigma_beta"),

tfd.HalfCauchy(loc=0., scale=sigma_error, name="sigma_error"),

tfd.Normal(loc=tf.zeros(k), scale=sigma_beta, name="beta"),

tfd.Gumbel(loc=0., scale=sigma_error, name="error"),

lambda beta, error: tfd.Deterministic(

tf.math.argmax(

tfd.Multinomial(

total_count=1,

logits=utilities(X_train, beta[..., tf.newaxis], error[..., tf.newaxis]),

#name="MNL"

).sample(), axis=-1

),

name="best_choices"

),

])

def target_log_prob(sigma_beta, sigma_error, beta, error):

return alt_pooled_model(X_train).log_prob(sigma_beta=sigma_beta, sigma_error=sigma_error, beta=beta, error=error,

best_choices=best_choices)

def utilities(x, betas, errors):

"""

`x * betas + errors` with broadcasting.

"""

x = tf.cast(x, dtype=tf.float32)

return tf.transpose(x) * betas + errors

k = 10

sigma_beta = 5.

sigma_error = 1.

def alt_pooled_model(X_train):

return tfd.JointDistributionSequential([

tfd.HalfCauchy(loc=0., scale=sigma_beta, name="sigma_beta"),

tfd.HalfCauchy(loc=0., scale=sigma_error, name="sigma_error"),

tfd.Normal(loc=tf.zeros(k), scale=sigma_beta, name="beta"),

tfd.Gumbel(loc=0., scale=sigma_error, name="error"),

lambda beta, error: tfd.Deterministic(

tf.math.argmax(

tfd.Multinomial(

total_count=1,

logits=utilities(X_train, beta[..., tf.newaxis], error[..., tf.newaxis]),

#name="MNL"

).sample(), axis=-1

),

name="best_choices"

),

])

def target_log_prob(sigma_beta, sigma_error, beta, error):

return alt_pooled_model(X_train).log_prob(sigma_beta=sigma_beta, sigma_error=sigma_error, beta=beta, error=error,

best_choices=best_choices)

# Use NUTS for inference

hmc = tfp.mcmc.NoUTurnSampler(

target_log_prob_fn=target_log_prob,

step_size=.01)

# Unconstrain the scale parameters, which must be positive

hmc = tfp.mcmc.TransformedTransitionKernel(

inner_kernel=hmc,

bijector=[

tfp.bijectors.Identity(), # sigma_beta

tfp.bijectors.Identity(), # sigma_error

tfp.bijectors.Identity(), # beta

tfp.bijectors.Identity(), # error

])

# Adapt the step size for 100 steps before burnin and main sampling

hmc = tfp.mcmc.DualAveragingStepSizeAdaptation(

inner_kernel=hmc,

num_adaptation_steps=100,

target_accept_prob=.75)

# Initialize 10 chains using samples from the prior

joint_sample = alt_pooled_model(X_train).sample(10)

initial_state = [

joint_sample[0],

joint_sample[1],

joint_sample[2],

joint_sample[3],

]

# Compile with tf.function and XLA for improved runtime performance

@tf.function(autograph=False, experimental_compile=True)

def run():

return tfp.mcmc.sample_chain(

num_results=500,

current_state=initial_state,

kernel=hmc,

num_burnin_steps=200,

trace_fn=lambda _, kr: kr)

samples, traces = run()

print('R-hat diagnostics: ', tfp.mcmc.potential_scale_reduction(samples))

hmc = tfp.mcmc.NoUTurnSampler(

target_log_prob_fn=target_log_prob,

step_size=.01)

# Unconstrain the scale parameters, which must be positive

hmc = tfp.mcmc.TransformedTransitionKernel(

inner_kernel=hmc,

bijector=[

tfp.bijectors.Identity(), # sigma_beta

tfp.bijectors.Identity(), # sigma_error

tfp.bijectors.Identity(), # beta

tfp.bijectors.Identity(), # error

])

# Adapt the step size for 100 steps before burnin and main sampling

hmc = tfp.mcmc.DualAveragingStepSizeAdaptation(

inner_kernel=hmc,

num_adaptation_steps=100,

target_accept_prob=.75)

# Initialize 10 chains using samples from the prior

joint_sample = alt_pooled_model(X_train).sample(10)

initial_state = [

joint_sample[0],

joint_sample[1],

joint_sample[2],

joint_sample[3],

]

# Compile with tf.function and XLA for improved runtime performance

@tf.function(autograph=False, experimental_compile=True)

def run():

return tfp.mcmc.sample_chain(

num_results=500,

current_state=initial_state,

kernel=hmc,

num_burnin_steps=200,

trace_fn=lambda _, kr: kr)

samples, traces = run()

print('R-hat diagnostics: ', tfp.mcmc.potential_scale_reduction(samples))

ValueError: Inconsistent names: component with name "beta" was referred to by a different name "error".

Please help

Feb 21, 2023, 7:10:56 PM2/21/23

to TensorFlow Probability, Surbhi Gupta

shape of X_train is (60000, 10) and best_choices is (60000, 1)

Feb 21, 2023, 7:12:08 PM2/21/23

to Surbhi Gupta, TensorFlow Probability

I think the issue is that when you use a lambda in `JointDistributionSequential`, the arguments to the lambda are most recent first (see the example here, particularly the line with two parameter lambda computing m from n and g). So I think you need to flip the order of the 'beta' and 'error' arguments to your lambda.

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Feb 21, 2023, 7:12:27 PM2/21/23

to Surbhi Gupta, TensorFlow Probability

The arguments to the lambda that is constructing the 'best_choices' distribution. They should be reversed.

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Feb 21, 2023, 7:41:54 PM2/21/23

to TensorFlow Probability, si...@google.com, TensorFlow Probability, Surbhi Gupta

Thanks a ton, the solves this error but running into dimensions error, debugging

Feb 21, 2023, 7:58:58 PM2/21/23

to TensorFlow Probability, Surbhi Gupta

I am running into this error now -

ValueError: Dimensions must be equal, but are 60000 and 10 for '{{node mcmc_sample_chain/dual_averaging_step_size_adaptation___init__/_bootstrap_results/transformed_kernel_bootstrap_results/NoUTurnSampler/.bootstrap_results/process_args/maybe_call_fn_and_grads/value_and_gradients/value_and_gradient/JointDistributionSequential/log_prob/best_choices/log_prob/sub}} = Sub[T=DT_INT64](mcmc_sample_chain/dual_averaging_step_size_adaptation___init__/_bootstrap_results/transformed_kernel_bootstrap_results/NoUTurnSampler/.bootstrap_results/process_args/maybe_call_fn_and_grads/value_and_gradients/value_and_gradient/Const_4, mcmc_sample_chain/dual_averaging_step_size_adaptation___init__/_bootstrap_results/transformed_kernel_bootstrap_results/NoUTurnSampler/.bootstrap_results/process_args/maybe_call_fn_and_grads/value_and_gradients/value_and_gradient/JointDistributionSequential/log_prob/ArgMax)' with input shapes: [60000], [10,10].

I have tried changing the argmax axis to 0 and 1 as well, still get some dimension error

On Tuesday, 21 February 2023 at 19:05:46 UTC-5 Surbhi Gupta wrote:

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