| import tensorflow as tf |
| import numpy as np |
| import numpy.polynomial.polynomial as poly |
| |
| from random import randint |
| |
| tf.reset_default_graph() |
| num_neurons = 5000 |
| |
| indices = [] |
| values = [] |
| for i in range(num_neurons): |
| for j in range(num_neurons): |
| x = 3 |
| if i != j: |
| number = randint(0, 99) |
| if number < 5: |
| indices.append([i, j]) |
| values.append(1.0/5) |
| |
| connections = tf.SparseTensor(indices=indices, values=values, dense_shape=[num_neurons, num_neurons]) |
| |
| neuron_values = tf.Variable(np.ones(num_neurons), dtype=tf.float32) |
| |
| mul_product = tf.sparse_tensor_dense_matmul(connections, tf.reshape(neuron_values, shape=(num_neurons, 1))) |
| |
| sess = tf.Session() |
| sess.run(tf.global_variables_initializer()) |
| |
| output = sess.run(mul_product) |
