Problem with input shape of Conv1d in tfagents sequential network

Question

I have created a trading environment using tfagent

env = TradingEnv(df=df.head(100000), lkb=1000)
tf_env = tf_py_environment.TFPyEnvironment(env)

and passed a df of 100000 rows from which only closing prices are used which a numpy array of 100000 stock price time series data

df: Date Open High Low Close volume
0 2015-02-02 09:15:00+05:30 586.60 589.70 584.85 584.95 171419
1 2015-02-02 09:20:00+05:30 584.95 585.30 581.25 582.30 59338
2 2015-02-02 09:25:00+05:30 582.30 585.05 581.70 581.70 52299
3 2015-02-02 09:30:00+05:30 581.70 583.25 581.70 582.60 44143
4 2015-02-02 09:35:00+05:30 582.75 584.00 582.75 582.90 42731
... ... ... ... ... ... ...
99995 2020-07-06 11:40:00+05:30 106.85 106.90 106.55 106.70 735032
99996 2020-07-06 11:45:00+05:30 106.80 107.30 106.70 107.25 1751810
99997 2020-07-06 11:50:00+05:30 107.30 107.50 107.10 107.35 1608952
99998 2020-07-06 11:55:00+05:30 107.35 107.45 107.10 107.20 959097
99999 2020-07-06 12:00:00+05:30 107.20 107.35 107.10 107.20 865438

at each step the agent has access to previous 1000 prices + current price of stock = 1001

and it can take 3 possible action from 0,1,2

then i wrapped it in TFPyEnvironment to convert it to tf_environment

the prices that the agent can observe is a 1d numpy array.

prices = [584.95 582.3 581.7... 107.35 107.2 107.2 ]

TimeStep Specs

TimeStep Specs: TimeStep( {'discount': BoundedTensorSpec(shape=(), dtype=tf.float32,                     
name='discount', minimum=array(0., dtype=float32), maximum=array(1., dtype=float32)), 
'observation': BoundedTensorSpec(shape=(1001,), dtype=tf.float32, name='_observation', 
minimum=array(0., dtype=float32), maximum=array(3.4028235e+38, dtype=float32)), 'reward': 
TensorSpec(shape=(), dtype=tf.float32, name='reward'), 'step_type': TensorSpec(shape=(), 
dtype=tf.int32, name='step_type')}) Action Specs: BoundedTensorSpec(shape=(), dtype=tf.int32, 
name='_action', minimum=array(0, dtype=int32), maximum=array(2, dtype=int32))

then i build a dqn agent but i want to build it with a Conv1d layer

my.network consist of Conv1D, MaxPool1D, Conv1D, MaxPool1D, Dense_64, Dense_32, q_value_layer

i created a list layers using tf.keras.layers api and stored it in dense_layers list and created a Sequential Network

DQN_Agent

`learning_rate = 1e-3
action_tensor_spec = tensor_spec.from_spec(tf_env.action_spec())
num_actions = action_tensor_spec.maximum - action_tensor_spec.minimum + 1

dense_layers = []

dense_layers.append(tf.keras.layers.Conv1D(
64,
kernel_size=(10),
activation=tf.keras.activations.relu,
input_shape=(1,1001),
))

dense_layers.append(
tf.keras.layers.MaxPool1D(
pool_size=2,
strides=None,
padding='valid',
))

dense_layers.append(tf.keras.layers.Conv1D(
64,
kernel_size=(10),
activation=tf.keras.activations.relu,
))

dense_layers.append(
tf.keras.layers.MaxPool1D(
pool_size=2,
strides=None,
padding='valid',
))

dense_layers.append(
tf.keras.layers.Dense(
64,
activation=tf.keras.activations.relu,
))

dense_layers.append(
tf.keras.layers.Dense(
32,
activation=tf.keras.activations.relu,
))

q_values_layer = tf.keras.layers.Dense(
num_actions,
activation=None,
kernel_initializer=tf.keras.initializers.RandomUniform(
minval=-0.03, maxval=0.03),
bias_initializer=tf.keras.initializers.Constant(-0.2))

q_net = sequential.Sequential(dense_layers + [q_values_layer])`

`optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

train_step_counter = tf.Variable(0)

agent = dqn_agent.DqnAgent(
tf_env.time_step_spec(),
tf_env.action_spec(),
q_network=q_net,
optimizer=optimizer,
td_errors_loss_fn=common.element_wise_squared_loss,
train_step_counter=train_step_counter)    

agent.initialize()`

but when i passed the q.net as a q.network to DqnAgent i came accross this error

`---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in ()
68 optimizer=optimizer,
69 td_errors_loss_fn=common.element_wise_squared_loss,
---> 70 train_step_counter=train_step_counter)
71
72 agent.initialize()

7 frames
/usr/local/lib/python3.7/dist-packages/tf_agents/networks/sequential.py in call(self, inputs,         
network_state, **kwargs)
222 else:
223 # Does not maintain state.
--> 224 inputs = layer(inputs, **layer_kwargs)
225
226 return inputs, tuple(next_network_state)

ValueError: Exception encountered when calling layer "sequential_54" (type Sequential).

Input 0 of layer "conv1d_104" is incompatible with the layer: expected min_ndim=3, found         
ndim=2. Full shape received: (1, 1001)

Call arguments received by layer "sequential_54" (type Sequential):
• inputs=tf.Tensor(shape=(1, 1001), dtype=float32)
• network_state=()
• kwargs={'step_type': 'tf.Tensor(shape=(1,), dtype=int32)', 'training': 'None'}
In call to configurable 'DqnAgent' (<class 'tf_agents.agents.dqn.dqn_agent.DqnAgent'>)`

i know it has something to do with the input shape of first layer of cov1d but cant figure out what am doing wrong

at each time_step the agent is reciveing a observation of prices of 1d array of length 1001 then the input shape of conv1d should be (1,1001) but its wrong and i don't know how to solve this error

need help

Answer 1

Unfortunately, TF-Agents doesn't support Conv1D layers . Almost all.network classes use the EncodingNetwork class to build their.networks. If You check out their github code or documentation, they do provide the Conv1D layer in the EncodingNetwork , however, it is set to Conv2D by default and no.network class has a parameter that sets the conv_type .

However, there is a workaround. Just copy the.network you want to use, and change the line that calls the EncodingNetwork, so that the conv_type is set to 1D . I had also open a github issue about that here:

https://github.com/tensorflow/agents/issues/779