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Conway's Game of Life using a neural network with Keras and Tensorflow in Python
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| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import tensorflow as tf | |
| from matplotlib.animation import FuncAnimation | |
| from tensorflow.keras.layers import Conv2D, InputLayer, Layer | |
| from tensorflow.keras.models import Sequential | |
| size = 10 | |
| full_size = (1, size, size, 1) | |
| padded_size = (1, size + 2, size + 2, 1) | |
| n_frames = 120 | |
| glider = ((1, 2), (2, 3), (3, 1), (3, 2), (3, 3)) | |
| # env = np.random.randint(0, 2, full_size) | |
| env = np.zeros(full_size, dtype=int) | |
| for pos in glider: | |
| env[(0,) + pos] = 1 | |
| class TorusPaddingLayer(Layer): | |
| def __init__(self, **kwargs): | |
| """Based on: https://stackoverflow.com/questions/39088489/tensorflow-periodic-padding""" | |
| super(TorusPaddingLayer, self).__init__(**kwargs) | |
| top_row = np.zeros((1, size)) | |
| bottom_row = np.zeros((1, size)) | |
| top_row[0, -1] = 1 | |
| bottom_row[-1, 0] = 1 | |
| self.pre = tf.convert_to_tensor(np.vstack((top_row, np.eye(size), bottom_row)), dtype=tf.int32) | |
| self.pre_T = tf.transpose(self.pre) | |
| def call(self, inputs): | |
| squeezed = tf.squeeze(inputs) | |
| result = self.pre @ squeezed @ self.pre_T | |
| result = tf.expand_dims(result, 0) | |
| result = tf.expand_dims(result, -1) | |
| return result | |
| torus_padding = TorusPaddingLayer() | |
| model = Sequential([InputLayer(input_shape=padded_size[1:]), | |
| Conv2D(1, 3, padding="valid", activation=None, use_bias=False, kernel_initializer="ones")]) | |
| frames = [] | |
| for i in range(n_frames): | |
| # 2D sliding window of 3x3 including summation | |
| # NOTE: convolve2d of scipy does support torus-padding but that's obviously not as cool as a neural network | |
| # TODO: Add custom layer to Sequential model, causing bugs at the moment | |
| padded = torus_padding(env) | |
| neighbours = (model(padded) - env) # Don't count the cell itself in the number of neighbours | |
| env = np.where((env & np.isin(neighbours, (2, 3))) | ((env == 0) & (neighbours == 3)), 1, 0) | |
| frames.append(env.squeeze()) | |
| fig = plt.figure() | |
| ax = plt.axes(xlim=(0, size), ylim=(0, size)) | |
| render = plt.imshow(frames[0], interpolation="none", cmap="binary") | |
| def animate(i: int): | |
| render.set_array(frames[i]) | |
| return [render] | |
| anim = FuncAnimation(fig, animate, frames=n_frames, interval=30, blit=True) | |
| plt.axis("off") | |
| plt.gca().invert_yaxis() | |
| anim.save("glider.gif", fps=30) | |
| plt.show() |
Hey I'm interested in your work. You got this compressed in a way that I haven't seen before. Can we zoom? I'm building a neuron based GOL based on a similar proximity algorithm. You may find it very interesting. Email me at [email protected] and we can schedule a time.
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10x10 glider
100x100 with random initialization