How to store intermediate convolution results in tensorflow - numpy

Question

please refer to "myalexnet_forward_tf2.py" in this link:
https://github.com/mikechen66/AlexNet_TensorFlow_2/tree/master/alexnet_original_tf2
There are 5 convolutions in Alexnet.
I want save individual intermediate convolution result with no bias adding as .npy using np.save() function
So I add code like below:
def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
'''From https://github.com/ethereon/caffe-tensorflow
'''
c_i = input.get_shape()[-1]
assert c_i%group==0
assert c_o%group==0
convolve = lambda i,k: tf.nn.conv2d(i,k,[1,s_h,s_w,1],padding=padding)
if group==1:
conv = convolve(input, kernel)
else:
input_groups = tf.split(input, group, 3) #tf.split(3, group, input)
kernel_groups = tf.split(kernel, group, 3) #tf.split(3, group, kernel)
output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)]
conv = tf.concat(output_groups, 3) #tf.concat(3, output_groups)
np.save("conv_golden", conv) # <-------- added code
print("conv input shape :", input.shape, ", filter shape :", kernel.shape, ", conv result(no bias) shape :", conv.shape)
return tf.reshape(tf.nn.bias_add(conv,biases), [-1]+conv.get_shape().as_list()[1:])
please check
np.save("conv_golden", conv) # <-------- added code
I just expected caculated convolution result (conv) automatically would be saved.
When I was executing this one, the error message said "
NotImplementedError: Cannot convert a symbolic tf.Tensor (Conv2D:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.
"
I don't know deep enough for tensorflow, but I guess tensorflow abstracts sequence, and sequence is executed when data is put in.
How can I save 5 individual intermediate convolution result?

Answer 1

This error is expected.
If you do not use tf.compat.v1.disable_eager_execution(), just use .numpy() method on the tensor and then save it.
import numpy as np
import tensorflow as tf
# Create a sample tensor
x = tf.constant([[10, 2], [33, 4]], dtype=tf.float32)
# Convert to numpy
x_np = x.numpy()
# Save the numpy array to disk
np.save('x_saved.npy', x_np)
If you use tf.compat.v1.disable_eager_execution(), then do the following:
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
import numpy as np
# Build the TensorFlow graph
a = tf.constant([10, 2, 3])
b = tf.constant([4, 5, 60])
c = tf.add(a, b)
# Create the session
with tf.compat.v1.Session() as sess:
x_saved = sess.run(c) # evaluate the tensor (this is the trick)
# Save the tensor
np.save("x_saved.npy", x_saved)