def _test(logits, n):
rv = Categorical(logits=logits)
rv_sample = rv.sample(n)
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.int32)
logits = logits.eval()
assert np.allclose(rv.log_prob(x_tf).eval(),
categorical_logpmf_vec(x, logits))
def _test(logits, n):
x = Categorical(logits=logits)
val_est = get_dims(x.sample(n))
val_true = n + get_dims(logits)[:-1]
assert val_est == val_true
def _test(logits, n):
x = Categorical(logits=logits)
val_est = get_dims(x.sample(n))
val_true = n + get_dims(logits)[:-1]
assert val_est == val_true
for epoch in tqdm(range(EPOCH_NUM)):
# Let the training begin. We load the data in minibatches and update the VI infernce using each new batch.
perm = np.random.permutation(N) #disorganize the data
for i in range(inference.n_iter): # from 0 to N interval is Batch size
batch_x = train_x[perm[i:i + batch]]
batch_y = train_y2[perm[i:i + batch]]
info_dict = inference.update(feed_dict={
x_: batch_x,
y_: batch_y,
keep_prob: 0.5
})
inference.print_progress(info_dict)
y_samples1 = y.sample(samples_num).eval(feed_dict={
x_: train_x,
keep_prob: 1
})
y_samples = y.sample(samples_num).eval(feed_dict={
x_: test_x,
keep_prob: 1
})
for i in range(samples_num):
acc = (y_samples[i] == test_y2).mean() * 100
temp = (y_samples1[i] == train_y2).mean() * 100
accy_test.append(acc)
accy_train.append(temp)
plt.hist(accy_test)
plt.title("Histogram of prediction accuracies in the test data")
plt.xlabel("Accuracy")