Basic 1d Convolution In Tensorflow

OK, I'd like to do a 1-dimensional convolution of time series data in Tensorflow. This is apparently supported using tf.nn.conv2d, according to these tickets, and the manual. the o

Solution 1:

I am sorry to say that, but your first code was almost right. You just inverted x and phi in tf.nn.conv2d:

g = tf.Graph()
with g.as_default():
    # data shape is "[batch, in_height, in_width, in_channels]",
    x = tf.Variable(np.array([0.0, 0.0, 0.0, 0.0, 1.0]).reshape(1, 1, 5, 1), name="x")
    # filter shape is "[filter_height, filter_width, in_channels, out_channels]"
    phi = tf.Variable(np.array([0.0, 0.5, 1.0]).reshape(1, 3, 1, 1), name="phi")
    conv = tf.nn.conv2d(
        x,
        phi,
        strides=[1, 1, 1, 1],
        padding="SAME",
        name="conv")

---

Update: TensorFlow now supports 1D convolution since version r0.11, using tf.nn.conv1d. I previously made a guide to use them in the stackoverflow documentation (now extinct) that I'm pasting here:

---

Guide to 1D convolution

Consider a basic example with an input of length 10, and dimension 16. The batch size is 32. We therefore have a placeholder with input shape [batch_size, 10, 16].

batch_size = 32
x = tf.placeholder(tf.float32, [batch_size, 10, 16])

We then create a filter with width 3, and we take 16 channels as input, and output also 16 channels.

filter = tf.zeros([3, 16, 16])  # these should be real values, not 0

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Finally we apply tf.nn.conv1d with a stride and a padding: - stride: integer s - padding: this works like in 2D, you can choose between SAME and VALID. SAME will output the same input length, while VALID will not add zero padding.

For our example we take a stride of 2, and a valid padding.

output = tf.nn.conv1d(x, filter, stride=2, padding="VALID")

The output shape should be [batch_size, 4, 16]. With padding="SAME", we would have had an output shape of [batch_size, 5, 16].

Solution 2:

In the new versions of TF (starting from 0.11) you have conv1d, so there is no need to use 2d convolution to do 1d convolution. Here is a simple example of how to use conv1d:

import tensorflow as tf
i = tf.constant([1, 0, 2, 3, 0, 1, 1], dtype=tf.float32, name='i')
k = tf.constant([2, 1, 3], dtype=tf.float32, name='k')

data   = tf.reshape(i, [1, int(i.shape[0]), 1], name='data')
kernel = tf.reshape(k, [int(k.shape[0]), 1, 1], name='kernel')

res = tf.squeeze(tf.nn.conv1d(data, kernel, stride=1, padding='VALID'))
with tf.Session() as sess:
    print sess.run(res)

To understand how conv1d is calculates, take a look at various examples

Solution 3:

I think I got it to work with the requirements that I needed. The comments/details of how it works are on the code:

import numpy as np

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

task_name = 'task_MNIST_flat_auto_encoder'
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, Y_train = mnist.train.images, mnist.train.labels # N x D
X_cv, Y_cv = mnist.validation.images, mnist.validation.labels
X_test, Y_test = mnist.test.images, mnist.test.labels

# data shape is "[batch, in_height, in_width, in_channels]",# X_train = N x D
N, D = X_train.shape
# think of it as N images with height 1 and width D.
X_train = X_train.reshape(N,1,D,1)
x = tf.placeholder(tf.float32, shape=[None,1,D,1], name='x-input')
#x = tf.Variable( X_train , name='x-input')# filter shape is "[filter_height, filter_width, in_channels, out_channels]"
filter_size, nb_filters = 10, 12# filter_size , number of hidden units/units# think of it as having nb_filters number of filters, each of size filter_size
W = tf.Variable( tf.truncated_normal(shape=[1, filter_size, 1,nb_filters], stddev=0.1) )
stride_convd1 = 2# controls the stride for 1D convolution
conv = tf.nn.conv2d(input=x, filter=W, strides=[1, 1, stride_convd1, 1], padding="SAME", name="conv")

with tf.Session() as sess:
    sess.run( tf.initialize_all_variables() )
    sess.run(fetches=conv, feed_dict={x:X_train})

thanks to Olivier for the help (see the discussion in his comments for further clarification).

---

Manually check it:

X_train_org = np.array([[0,1,2,3]])
N, D = X_train_org.shape
X_train_1d = X_train_org.reshape(N,1,D,1)
#X_train = tf.constant( X_train_org )
# think of it as N images with height 1and width D.
xx = tf.placeholder(tf.float32, shape=[None,1,D,1], name='xx-input')
#x = tf.Variable( X_train , name='x-input')
# filter shape is "[filter_height, filter_width, in_channels, out_channels]"
filter_size, nb_filters = 2, 2 # filter_size , number of hidden units/units
# think of it as having nb_filters number of filters, each of size filter_size
filter_w = np.array([[1,3],[2,4]]).reshape(1,filter_size,1,nb_filters)
#W = tf.Variable( tf.truncated_normal(shape=[1,filter_size,1,nb_filters], stddev=0.1) )
W = tf.Variable( tf.constant(filter_w, dtype=tf.float32) )
stride_convd1 = 2 # controls the stride for1D convolution
conv = tf.nn.conv2d(input=xx, filter=W, strides=[1, 1, stride_convd1, 1], padding="SAME", name="conv")

#C = tf.constant( (np.array([[4,3,2,1]]).T).reshape(1,1,1,4) , dtype=tf.float32 ) #
#tf.reshape( conv , [])
#y_tf = tf.matmul(conv, C)


##
x = tf.placeholder(tf.float32, shape=[None,D], name='x-input') # N x 4
W1 = tf.Variable( tf.constant( np.array([[1,2,0,0],[3,4,0,0]]).T, dtype=tf.float32 ) ) # 2 x 4
y1 = tf.matmul(x,W1) # N x 2 = N x 4 x 4 x 2
W2 = tf.Variable( tf.constant( np.array([[0,0,1,2],[0,0,3,4]]).T, dtype=tf.float32 ))
y2 = tf.matmul(x,W2) # N x 2 = N x 4 x 4 x 2
C1 = tf.constant( np.array([[4,3]]).T, dtype=tf.float32 ) # 1 x 2
C2 = tf.constant( np.array([[2,1]]).T, dtype=tf.float32 )

p1 = tf.matmul(y1,C1)
p2 = tf.matmul(y2,C2)
y = p1 + p2
with tf.Session() as sess:
    sess.run( tf.initialize_all_variables() )
    print'manual conv'print sess.run(fetches=y1, feed_dict={x:X_train_org})
    print sess.run(fetches=y2, feed_dict={x:X_train_org})
    #print sess.run(fetches=y, feed_dict={x:X_train_org})
    print'tf conv'print sess.run(fetches=conv, feed_dict={xx:X_train_1d})
    #print sess.run(fetches=y_tf, feed_dict={xx:X_train_1d})

outputs:

manual conv
[[ 2.  4.]][[  8.  18.]]
tf conv
[[[[  2.   4.]
   [  8.  18.]]]]