Inconsistencies between tf.contrib.layer.fully_connected, tf.layers.dense, tf.contrib.slim.fully_connected, tf.keras.layers.Dense

Question

I am trying to implement policy gradient for a contextual bandit problem ( https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-1-5-contextual-bandits-bff01d1aad9c ).

I am defining a model in tensorflow to solve this problem using a single fully-connected layer.

I am trying out different APIs from tensorflow, but want to avoid using the contrib package since it is not tensorflow-supported. I am interested in using the keras API since I am already familiar with the functional interface, and it is now implemented as tf.keras . However, I can only seem to get results to work when using tf.contrib.slim.fully_connected , or tf.contrib.layers.fully_connected (the former calls the latter).

The following two snippets work correctly ( one_hot_encoded_state_input and num_actions both adhere to the expected tensor shapes for the layers).

import tensorflow.contrib.slim as slim
action_probability_distribution = slim.fully_connected(
    one_hot_encoded_state_input, \
    num_actions, \     
    biases_initializer=None, \
    activation_fn=tf.nn.sigmoid, \
    weights_initializer=tf.ones_initializer())

and

from tensorflow.contrib.layers import fully_connected
action_probability_distribution = fully_connected(
    one_hot_encoded_state_input,
    num_actions,\
    biases_initializer=None, \
    activation_fn=tf.nn.sigmoid, \
    weights_initializer=tf.ones_initializer())

On the other hand, neither of the following work:

action_probability_distribution = tf.layers.dense(
    one_hot_encoded_state_input, \
    num_actions, \
    activation=tf.nn.sigmoid, \
    bias_initializer=None, \
    kernel_initializer=tf.ones_initializer())

nor

action_probability_distribution = tf.keras.layers.Dense(
    num_actions, \
    activation='sigmoid', \
    bias_initializer=None, \
    kernel_initializer = 'Ones')(one_hot_encoded_state_input)

The last two cases use tensorflow's high level APIs layers and keras . Ideally, I would like to know if I am incorrectly implementing the first two cases using the last two cases , and if the only issue I am having is that the latter two are not equivalent to the former two .

For completeness, here is the entire code needed to run this (Note: python 3.5.6 and tensorflow 1.12.0 were used).

import tensorflow as tf
import numpy as np
tf.reset_default_graph()

num_states = 3
num_actions = 4
learning_rate = 1e-3

state_input = tf.placeholder(shape=(None,),dtype=tf.int32, name='state_input')
one_hot_encoded_state_input = tf.one_hot(state_input, num_states)

# DOESN'T WORK
action_probability_distribution = tf.keras.layers.Dense(num_actions, activation='sigmoid', bias_initializer=None, kernel_initializer = 'Ones')(one_hot_encoded_state_input)

# WORKS
# import tensorflow.contrib.slim as slim
# action_probability_distribution = slim.fully_connected(one_hot_encoded_state_input,num_actions,\
#     biases_initializer=None,activation_fn=tf.nn.sigmoid,weights_initializer=tf.ones_initializer())

# WORKS
# from tensorflow.contrib.layers import fully_connected
# action_probability_distribution = fully_connected(one_hot_encoded_state_input,num_actions,\
#     biases_initializer=None,activation_fn=tf.nn.sigmoid,weights_initializer=tf.ones_initializer())

# DOESN'T WORK
# action_probability_distribution = tf.layers.dense(one_hot_encoded_state_input,num_actions, activation=tf.nn.sigmoid, bias_initializer=None, kernel_initializer=tf.ones_initializer())

action_probability_distribution = tf.squeeze(action_probability_distribution)
action_chosen = tf.argmax(action_probability_distribution)

reward_input = tf.placeholder(shape=(None,), dtype=tf.float32, name='reward_input')
action_input = tf.placeholder(shape=(None,), dtype=tf.int32, name='action_input')
responsible_weight = tf.slice(action_probability_distribution, action_input, [1])
loss = -(tf.log(responsible_weight)*reward_input)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
update = optimizer.minimize(loss)


bandits = np.array([[0.2,0,-0.0,-5],
                    [0.1,-5,1,0.25],
                    [-5,5,5,5]])

assert bandits.shape == (num_states, num_actions)

def get_reward(state, action): # the lower the value of bandits[state][action], the higher the likelihood of reward
    if np.random.randn() > bandits[state][action]:
        return 1
    return -1

max_episodes = 10000
epsilon = 0.1

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    rewards = np.zeros(num_states)
    for episode in range(max_episodes):
        state = np.random.randint(0,num_states)
        action = sess.run(action_chosen, feed_dict={state_input:[state]})
        if np.random.rand(1) < epsilon:
            action = np.random.randint(0, num_actions)

        reward = get_reward(state, action)
        sess.run([update, action_probability_distribution, loss], feed_dict = {reward_input: [reward], action_input: [action], state_input: [state]})

        rewards[state] += reward

        if episode%500 == 0:
            print(rewards)

When using the chunks commented # THIS WORKS , the agent learns and maximizes reward across all three states. On the other hand, those commented # THIS DOESN'T WORK# don't learn and typically converge extremely quickly to choosing one action. For example, working behaviour should print a reward list that is positive, increasing numbers (good cumulative reward for each state). non-working behaviour looks like a reward list that has only one action with increasing cumulative reward, usually sacrificing the other (negative cumulative reward).

Answer 1

For anyone who runs into this issue, especially since tensorflow has many APIs for implementation, the difference comes down to bias initialization and defaults. For tf.contrib and tf.slim , using biases_initializer = None means that no bias is used. Replicating this using tf.layers and tf.keras requires use_bias=False .