1 dimenstional convolution error in using tensorflow - tensorflow

Question

I am studying 1d convolution using tensorflow.
Code:
import numpy as np
import tensorflow as tf
\#####raw data, input length is 24, and feature_len is 6
batch = np.ceil((np.random.rand(24, 6)*10))-5
\#####filter for convoltion, filter width is 3, filter input dim is 6, output dim is 18
eye_filter = tf.constant(np.eye(3*6).reshape(3,6,18).reshape(3,6,18))
\#####here error happened
conv = tf.nn.conv1d(input=batch, filters=eye_filter, stride=1, padding='SAME')
Error Message:
InvalidArgumentError Traceback (most recent call
last)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py
in _create_c_op(graph, node_def, inputs, control_inputs) 1606
try:
-> 1607 c_op = c_api.TF_FinishOperation(op_desc) 1608 except errors.InvalidArgumentError as e:
InvalidArgumentError: Shape must be rank 4 but is rank 3 for
'conv1d_1' (op: 'Conv2D') with input shapes: [24,1,6], [1,3,6,18].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call
last) 10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py
in _create_c_op(graph, node_def, inputs, control_inputs) 1608
except errors.InvalidArgumentError as e: 1609 # Convert to
ValueError for backwards compatibility.
-> 1610 raise ValueError(str(e)) 1611 1612 return c_op
ValueError: Shape must be rank 4 but is rank 3 for 'conv1d_1' (op:
'Conv2D') with input shapes: [24,1,6], [1,3,6,18].
Why is filter rank 4, when I reshaped it to 3?
Why is op name is Conv2D, when I did conv1d?
How can I see the convolution result of above two tensor(raw data and filter)?

Answer 1

It's expecting your input tensor to be "Rank 4" meaning it has 4 dimensions, but you've technically given a 2d array.
Technically, Conv1d uses Conv2d as you noticed, according to this API documentation:
conv1d api doc
Your array for input data has a length of 24, and 6 channels for the features.
The TF convolution functions can operate on an array of inputs.
This means your data also has to have an index for which element out of the batch of inputs you want to select. I'm guessing from your example that you want to pass it just one input. To fix this, you need to reshape your tensor to have this extra dimension, but be length 1.
Really, conv1d only needs your input to be rank 3, but it transparently inserts a new dimension of length 1 so it's 2d (Imagine a monitor with resolution 1920x1. Technically 2d, but only 1 pixel high). Then it passes that to conv2d
Instead of keeping the data as a np array, use this function and then reshape it to be [Nth item (length 1)][Width (length 24)][Channel (length 6)]
Here's how I would rewrite your code:
import numpy as np
import tensorflow as tf
#####raw data, input length is 24, and feature_len is 6
batch = np.ceil((np.random.rand(24, 6)*10))-5
batch = tf.convert_to_tensor(batch, dtype=int32)
batch = tf.reshape(batch, shape=[1, 24, 6], dtype=int32)
#####filter for convoltion, filter width is 3, filter input dim is 6, output dim is 18
eye_filter = tf.constant(np.eye(3*6).reshape(3,6,18).reshape(3,6,18))
#####here error happened
# I added the optional data_format parameter
conv = tf.nn.conv1d(input=batch, data_format='NWC', filters=eye_filter, stride=1, padding='SAME')
I chose that specific shape ordering from the conv1d api doc about the data_format parameter having a default of "NWC" or Nth_item Width Channels. In conv2d, it has "NCHW" or similar. I would make sure you understand how that works so in the future you don't get weird results from an array that's shaped a way you didn't expect.
If you want to see the tensor output, you need to either make a graph and run it in a session. Or you can turn on eager execution.
sess = tf.Session()
print(sess.run(conv))
sess.close()
eager execution
Generally, you would use a session for speed with large computations, and use eager execution for debugging, learning, or verifying data is getting imported correctly.