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How to apply gradients later in tensorflow

I have a model where I computed the gradients manually over multiple examples. I have added the gradients manually, and now would like to do back propagation in tensorflow through:

prev_accum_grads = [tf.placeholder_with_default(input=tf.zeros(shape=var.get_shape().as_list(), dtype=m_dtype),
shape=var.get_shape().as_list(), name=var.name[:-2] + "_accum_grads")
for var in tf.trainable_variables()]

grads_and_vars = list(zip(prev_accum_grads, tf.trainable_variables()))
train_step = optimizer.apply_gradients(grads_and_vars)


Now, given the calculated gradients in prev_g[0], prev_g[1] ... prev_g[9] as in the code below; and would like to apply the gradients as:

prev_g[0], prev_g[1] ... prev_g[9]

# prev_g is a list holding the values of the gradients.
feed_dict = {
prev_accum_grads[0]: prev_g[0], prev_accum_grads[1]: prev_g[1], prev_accum_grads[2]: prev_g[2],
prev_accum_grads[3]: prev_g[3], prev_accum_grads[4]: prev_g[4], prev_accum_grads[5]: prev_g[5],
prev_accum_grads[6]: prev_g[6], prev_accum_grads[7]: prev_g[7], prev_accum_grads[8]: prev_g[8],
prev_accum_grads[9]: prev_g[9],
}

_, train_summaries = sess.run([train_step, train_merged_summaries],
feed_dict=feed_dict)


so this code is throwing an error asking me to provide value to the input placeholder.

input

so how to solve this problem?

Any help is much appreciated!!

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