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fix broken cell #470

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fix broken cell #470

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6 changes: 3 additions & 3 deletions Chapter1_Introduction/Ch1_Introduction_TFP.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1208,7 +1208,7 @@
"# Set the chain's start state.\n",
"initial_chain_state = [\n",
" tf.cast(tf.reduce_mean(count_data), tf.float32) * tf.ones([], dtype=tf.float32, name=\"init_lambda1\"),\n",
" tf.cast(tf.reduce_mean(count_data), tf.float32) * tf.ones([], dtype=tf.float32, name=\"init_lambda2\", tf.float32),\n",
" tf.cast(tf.reduce_mean(count_data), tf.float32) * tf.ones([], dtype=tf.float32, name=\"init_lambda2\"),\n",
" 0.5 * tf.ones([], dtype=tf.float32, name=\"init_tau\"),\n",
"]\n",
"\n",
Expand Down Expand Up @@ -1273,11 +1273,11 @@
" target_log_prob_fn=unnormalized_log_posterior,\n",
" num_leapfrog_steps=2,\n",
" step_size=step_size,\n",
" step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(),\n",
" step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(None),\n",
" state_gradients_are_stopped=True),\n",
" bijector=unconstraining_bijectors))\n",
"\n",
"tau_samples = tf.floor(posterior_tau * tf.cast(tf.size(count_data)), tf.float32)\n",
"tau_samples = tf.floor(posterior_tau * tf.cast(tf.size(count_data), tf.float32))\n",
"\n",
"# tau_samples, lambda_1_samples, lambda_2_samples contain\n",
"# N samples from the corresponding posterior distribution\n",
Expand Down