Set weight and bias tensors of tensorflow conv2d operation
I have been given a trained neural network in torch and I need to rebuild it exactly in tensorflow. I believe I have correctly defined the network's architecture in tensorflow but I am having trouble transferring the weight and bias tensors. Using a third party package, I converted all the weight and bias tensors from the torch network to numpy arrays then wrote them to disk. I can load them back into my python program but I cannot figure out a way to assign them to the corresponding layers in my tensorflow network.
For instance, I have a convolution layer defined in tensorflow as
kernel_1 = tf.Variable(tf.truncated_normal([11,11,3,64], stddev=0.1))
conv_kernel_1 = tf.nn.conv2d(input, kernel_1, [1,4,4,1], padding='SAME')
biases_1 = tf.Variable(tf.zeros[64])
bias_layer_1 = tf.nn_add(conv_kernel_1, biases_1)
According to the tensorflow documentation, the tf.nn.conv2d operation uses the shape defined in the kernel_1 variable to construct the weight tensor. However, I cannot figure out how to access that weight tensor to set it to the weight array I have loaded from file.
Is it possible to explicitly set the weight tensor? And if so, how?
(The same question applies to bias tensor.)
If you have the weights and biases in a NumPy array, it should be easy to connect them into your TensorFlow network:
weights_1_array = ... # ndarray of weights for layer 1
biases_1_array = ... # ndarray of biases for layer 1
conv_kernel_1 = tf.nn.conv2d(input, weights_1_array, [1, 4, 4, 1], padding='SAME')
bias_layer_1 = tf.nn.bias_add(conv_kernel_1, biases_1_array)
Note that you must ensure that weights_1_array and biases_1_array are in the correct data format. See the documentation for tf.nn.conv2d() for an explanation of the required filter shape.
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