Confused on how tensorflow feed
Recently started using tensorflow and I'm really confused on the functionality of feed_dict.
Looking at the mnist example from the tensorflow website, x is a symbolic placeholder that will be filled with a new batch of images every training iteration, so 'None' here could also be 'batch_size'
x = tf.placeholder(tf.float32, shape=[None, 784])
when looking at the convolutional part of this tutorial, there's a command to reshape x from it's flattened 1x784 shape back to a 2D image 28x28 shape
x_image = tf.reshape(x, [-1,28,28,1])
during the training loop, x is fed through the command
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
My question is when we feed in values to x, does tensorflow automatically vectorize every op involving x? So for example when we define the op
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
this will automatically work across the entire batch?
if x is ndarray with each row being a flattened image, because we specified shape 'None' in the x placeholder tensorflow automatically knows to use each row as an individual training sample, and vectorize all subsequent ops?
The shape argument is used for static shape inference (ie, tensor.get_shape ) and is optional. TensorFlow doesn't vectorize anything automatically, but for binary cwise ops it uses broadcasting which looks a bit like that. In your example, tf.conv2d is an operation that treats each row as an example, so it works with batches, but not with individual examples. Also batch[0] is a batch of inputs, and batch[1] is a batch of labels.
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