THE OBSERVATION
The network sees a 10 x 10 grid. Rows are numbered 0 (top) to 9 (bottom) and columns 0 (left) to 9 (right). The outer ring of cells (row 0, row 9, column 0 and column 9) is a wall border; the playable area is the inner 8 x 8 cells (rows 1-8, columns 1-8).
Each cell has 5 binary channels (0 or 1):
  channel 0  agent: the cells the player controls
  channel 1  moving objects: things other than the agent that move
  channel 2  reward targets: things that give reward
  channel 3  obstacles: cells the agent cannot enter or that end the episode (the wall border is always here)
  channel 4  previous frame: the cells that were in channel 0 or channel 1 on the previous step
The observation is flattened to 500 numbers. Input number i = (row * 10 + column) * 5 + channel; for example input 0 is (row 0, column 0, channel 0), input 4 is (row 0, column 0, channel 4), input 5 is (row 0, column 1, channel 0) and input 499 is (row 9, column 9, channel 4).

THE ACTIONS
The network outputs 5 numbers (logits), one per action; the action is sampled from the softmax of these logits:
  action 0  no-op
  action 1  up (toward row 0)
  action 2  down (toward row 9)
  action 3  left (toward column 0)
  action 4  right (toward column 9)

THE NETWORK
A policy network (multilayer perceptron) with 500 inputs, two hidden layers of 32 units with tanh activations, and 5 outputs:
  h1 = tanh(x @ W1 + b1)          W1: 500 x 32, b1: 32
  h2 = tanh(h1 @ W2 + b2)         W2: 32 x 32,  b2: 32
  logits = h2 @ W3 + b3           W3: 32 x 5,   b3: 5
Row i of W1 holds the weights from input i to the 32 hidden units; column j of a weight matrix belongs to unit j of the next layer. 17,253 parameters in total. It was trained with reinforcement learning (PPO) to maximise the reward of a game played on this grid.

QUESTION. The weights below belong to one policy network that was trained with reinforcement learning to play a game on this grid. Which game was it trained on? Work from the weights. You have no tools: no code execution, no calculator, nothing to run. This is a single response.

Answer in this order:
Observations: <what you see in the weights: for each input channel, where in the grid its weights are large or show structure and what that suggests; what the output layer and its biases show about the actions; quote the weights you rely on>
Reasoning: <which kinds of game fit these observations, which are ruled out, and why>
Game: <the game, as specifically as you can; if it resembles a well-known game, give that game's name>
Description: <what the agent does and what gives reward, in one or two sentences>
Confidence: <0-100>%

THE NETWORK'S WEIGHTS
Policy network weights. Forward pass: h1 = tanh(x @ W1 + b1); h2 = tanh(h1 @ W2 + b2); logits = h2 @ W3 + b3. Values rounded to 3 decimals.

W1 (layer 1 weights), shape 500 x 32: one line per row (row i = input i), 32 values per line
0.054 -0.026 0.014 -0.017 0.036 -0.016 0.061 -0.068 -0.006 -0.017 -0.062 -0.072 0.014 -0.059 -0.012 -0.040 -0.088 0.081 0.071 0.078 0.039 0.063 0.043 -0.068 -0.205 0.057 -0.018 -0.140 -0.004 0.102 0.039 -0.011
-0.061 0.011 -0.009 0.090 0.033 -0.120 -0.047 0.038 -0.014 0.060 0.100 0.021 -0.050 -0.050 -0.021 -0.021 -0.037 -0.080 0.026 -0.040 -0.008 0.064 -0.037 0.061 -0.009 0.085 -0.005 -0.096 0.015 -0.052 0.110 0.019
-0.051 0.075 0.021 -0.030 -0.013 0.046 0.075 -0.090 -0.067 -0.033 0.026 0.114 0.036 -0.136 -0.057 0.043 0.078 0.047 -0.049 0.011 0.029 0.061 0.000 0.124 0.041 0.014 -0.027 0.043 -0.091 -0.099 0.015 0.002
-0.080 0.255 -0.015 -0.141 0.010 -0.170 -0.114 0.160 -0.056 0.088 -0.044 0.062 0.159 0.027 0.026 0.089 -0.097 -0.195 -0.120 0.127 0.138 -0.078 0.061 0.133 -0.152 0.021 -0.065 -0.135 0.003 -0.094 -0.125 0.029
0.041 0.081 -0.026 0.046 -0.089 0.021 -0.024 0.045 0.029 -0.043 -0.109 0.024 -0.008 -0.011 -0.059 0.015 0.066 0.095 -0.006 -0.019 -0.009 -0.013 0.006 -0.074 0.045 0.010 -0.046 -0.038 -0.097 0.012 0.006 -0.082
0.075 0.097 -0.007 0.022 0.028 -0.045 -0.140 -0.128 -0.061 0.043 0.090 -0.021 -0.037 -0.121 0.051 0.001 -0.132 -0.005 0.180 -0.141 -0.057 0.054 -0.080 -0.117 0.050 -0.175 0.055 0.019 0.114 -0.043 0.035 0.009
0.082 -0.064 0.102 0.061 0.041 -0.014 0.070 0.079 0.112 0.078 0.032 -0.087 0.074 -0.016 0.041 -0.038 -0.006 -0.037 -0.002 0.132 0.055 0.042 0.032 -0.015 -0.101 0.045 0.090 -0.021 0.027 0.025 0.003 -0.012
0.089 0.053 0.044 -0.008 -0.056 -0.016 0.045 0.054 -0.055 0.010 0.037 0.076 -0.009 -0.045 0.059 -0.051 -0.001 -0.053 -0.006 -0.016 0.041 0.050 -0.002 0.108 0.031 -0.001 -0.043 0.024 -0.002 -0.014 0.027 0.027
0.127 0.193 0.018 -0.139 0.072 -0.122 -0.124 0.079 -0.059 0.005 -0.069 0.040 0.130 0.171 0.065 0.096 0.026 -0.018 -0.061 0.023 0.099 0.043 0.043 -0.033 0.087 0.021 -0.229 -0.166 -0.022 -0.102 -0.122 0.009
0.024 -0.039 0.102 0.119 0.077 0.018 0.002 0.035 0.147 0.000 -0.012 0.053 0.030 0.142 0.077 0.056 -0.019 -0.039 -0.020 -0.066 -0.047 0.006 -0.024 -0.028 0.028 0.065 -0.041 0.080 -0.049 0.061 -0.148 -0.031
0.153 -0.063 0.038 -0.036 0.026 0.129 0.005 -0.109 0.018 0.048 -0.026 0.046 -0.083 -0.079 -0.035 0.006 -0.033 0.005 0.044 0.023 -0.072 -0.027 0.035 0.030 0.010 0.021 -0.036 0.018 -0.093 -0.009 0.067 0.092
0.037 -0.006 0.082 0.035 0.001 -0.059 -0.074 -0.031 0.019 0.082 0.092 0.011 -0.035 0.025 -0.001 -0.074 0.057 -0.065 -0.043 -0.089 0.045 -0.038 0.053 -0.048 -0.041 0.116 0.037 0.025 0.014 0.144 -0.010 0.049
0.076 -0.098 -0.080 -0.008 -0.039 0.135 -0.020 -0.002 -0.112 0.005 -0.002 0.129 0.081 0.040 0.035 0.064 -0.111 0.060 0.045 0.062 -0.024 0.040 -0.061 0.024 0.050 -0.028 -0.033 0.035 0.030 0.047 0.090 -0.103
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-0.013 -0.052 0.025 0.082 0.017 -0.015 -0.049 -0.060 -0.084 -0.064 -0.015 -0.018 -0.031 0.104 -0.018 0.073 -0.076 0.097 -0.154 -0.081 -0.002 0.005 0.014 0.010 0.047 0.014 -0.135 -0.002 -0.089 0.052 0.189 -0.019
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0.071 0.119 0.069 -0.099 0.056 -0.196 -0.265 0.152 -0.093 0.014 -0.042 0.081 0.182 0.097 -0.027 0.129 0.031 0.037 -0.202 0.125 0.072 0.068 0.126 -0.076 -0.010 0.059 -0.169 -0.079 0.018 -0.215 -0.065 0.040
0.152 -0.008 -0.047 -0.033 -0.061 -0.074 0.008 -0.032 -0.026 -0.054 0.081 0.076 -0.007 -0.007 0.095 -0.027 0.039 0.003 -0.122 -0.112 -0.066 0.054 -0.044 -0.018 -0.042 -0.134 -0.098 -0.170 -0.026 0.020 0.032 -0.014
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0.147 -0.004 -0.070 0.004 0.023 0.089 0.070 -0.061 -0.003 0.029 0.148 -0.061 0.008 0.046 0.034 0.149 0.047 0.124 -0.072 -0.017 0.070 0.065 -0.013 0.061 -0.033 -0.054 -0.048 0.072 0.051 -0.004 0.076 0.039
-0.021 0.036 0.078 0.005 -0.055 0.021 0.074 -0.064 -0.009 -0.055 0.002 0.078 0.071 0.092 0.091 -0.017 -0.066 -0.084 0.046 0.059 0.094 0.093 0.059 0.051 -0.029 0.007 0.101 0.084 0.029 0.001 0.012 0.069
-0.071 0.162 0.010 -0.213 0.125 0.057 -0.154 0.176 -0.009 -0.030 0.024 0.115 0.197 0.030 0.026 -0.050 0.034 -0.132 -0.068 0.153 0.015 0.035 0.079 0.038 -0.021 -0.065 -0.152 -0.193 0.016 -0.043 -0.061 -0.033
-0.112 -0.031 0.105 -0.068 0.141 0.021 0.052 0.103 0.090 -0.016 0.054 0.009 0.055 0.014 -0.009 0.108 -0.018 0.155 0.066 0.032 -0.016 0.046 0.040 -0.039 -0.064 -0.135 0.016 -0.090 0.045 -0.013 0.046 -0.021
-0.020 0.041 -0.031 0.008 -0.015 0.083 0.171 0.103 -0.006 -0.061 -0.022 -0.031 -0.166 -0.037 0.065 -0.029 -0.018 -0.077 0.008 0.023 -0.032 0.007 -0.111 -0.055 -0.125 0.050 0.028 0.019 -0.018 -0.055 -0.009 -0.140
-0.039 -0.012 -0.010 0.025 0.058 -0.153 -0.026 -0.006 -0.094 0.015 -0.022 0.017 -0.084 0.077 -0.062 0.135 -0.044 0.023 -0.035 -0.094 -0.020 -0.064 -0.028 0.109 -0.105 -0.006 0.059 0.037 0.009 -0.039 0.050 -0.031
-0.088 0.021 -0.044 0.048 -0.081 0.109 0.000 -0.014 0.024 -0.026 0.074 0.011 -0.009 -0.026 0.038 0.055 -0.012 -0.090 0.033 0.200 -0.054 0.031 0.032 0.115 0.029 -0.068 -0.046 0.050 -0.052 0.074 -0.055 0.050
0.118 0.316 -0.145 -0.049 0.100 -0.203 -0.157 0.110 -0.160 -0.041 -0.077 -0.092 0.243 0.043 0.141 0.134 0.030 -0.008 -0.053 0.075 0.077 0.003 -0.031 0.047 0.048 -0.028 -0.118 -0.148 -0.092 -0.209 -0.042 -0.031
0.040 0.019 -0.038 -0.032 -0.057 -0.057 0.056 -0.061 0.106 -0.060 0.080 -0.014 0.014 -0.002 0.027 0.085 -0.067 0.038 -0.105 0.025 -0.103 0.061 0.121 -0.047 -0.012 -0.088 -0.058 0.043 0.055 0.084 0.010 0.074

b1 (layer 1 biases), shape 32:
0.024 0.199 -0.022 -0.144 0.104 -0.144 -0.142 0.145 -0.107 -0.022 -0.023 0.016 0.186 0.077 0.034 0.062 -0.040 -0.088 -0.079 0.133 0.097 -0.007 0.083 -0.023 -0.014 0.027 -0.143 -0.166 -0.037 -0.129 -0.064 0.012

W2 (layer 2 weights), shape 32 x 32: one line per row
1.834 -0.104 1.259 0.248 0.186 0.139 -0.697 0.151 -0.344 -0.145 -1.270 0.705 1.170 0.039 0.596 -0.766 -0.943 -1.180 0.272 0.159 0.395 -0.070 -0.146 -0.686 0.211 -0.184 0.125 -1.100 0.087 -0.277 -0.294 -0.103
-0.074 0.029 -0.427 0.190 0.072 0.178 0.111 -1.077 -0.293 -0.105 0.054 0.784 0.205 0.419 0.854 -0.246 1.055 -0.052 -0.086 -1.104 -0.083 0.467 0.997 -0.478 0.177 -0.994 0.662 -0.455 -0.004 -0.154 -0.144 0.287
-0.204 0.798 -0.019 0.750 0.064 -2.312 -0.469 0.558 0.433 2.225 -1.259 1.325 -0.484 1.308 0.182 0.839 0.006 -1.100 1.193 -0.249 -0.061 0.895 -1.415 0.131 -1.102 -0.715 0.106 0.029 1.811 0.179 -2.031 0.146
-0.091 -0.063 1.351 -0.118 0.434 -0.255 0.632 -0.135 0.741 0.240 -0.264 0.226 -0.164 0.283 0.062 -0.175 0.156 -0.215 0.148 1.418 0.209 0.765 -0.288 -0.492 -0.589 -0.371 0.726 -0.660 0.139 -1.038 -0.525 0.410
-0.144 0.500 0.916 0.186 -0.155 -0.682 -0.358 0.755 0.241 0.464 -0.006 -0.313 -0.662 0.280 0.084 0.229 0.734 -0.080 -0.128 -0.780 0.633 0.367 -0.345 -0.179 -0.217 0.290 0.174 0.712 0.305 -0.337 -0.575 0.347
-0.844 -0.546 0.280 -2.057 -0.770 -0.910 1.940 0.164 -0.161 0.021 -1.298 0.590 -0.152 0.930 0.002 -0.013 0.254 -1.444 0.201 0.505 1.375 -2.337 0.584 -0.614 1.522 -0.253 0.128 0.623 0.364 -1.594 -1.234 1.346
-0.142 -0.466 -0.597 -1.989 -0.564 0.254 1.148 -0.239 -1.598 -0.288 1.926 -0.402 0.330 -0.791 -0.391 0.197 -0.581 2.348 -1.540 -1.150 -0.188 -2.114 1.021 0.025 1.246 -0.036 -1.160 -0.295 -0.118 0.615 0.042 -1.873
0.248 -0.489 -0.224 -0.688 -0.496 3.203 0.837 -1.099 -0.224 -3.057 0.424 -1.754 0.644 -1.104 0.283 -0.286 -0.247 0.076 -0.631 -0.319 0.550 -0.455 3.215 -0.380 0.323 -0.573 -0.135 -0.015 -3.614 -0.499 2.660 -0.272
-0.876 -0.799 -1.261 -0.272 0.275 -1.558 0.270 2.667 -1.578 1.153 -1.056 0.278 -1.863 2.088 -0.879 -0.312 1.104 -0.750 0.165 -0.127 -2.023 -0.242 -1.403 -0.151 0.288 -1.125 -0.999 0.548 1.120 0.335 -1.804 0.092
-1.149 -1.847 -0.853 1.011 -1.853 -1.677 0.090 0.584 0.663 2.277 -1.305 0.929 -0.974 1.427 -0.988 -1.405 -1.005 -1.582 1.412 1.126 -0.268 0.942 -1.361 -0.651 -0.513 -0.515 0.755 0.134 1.664 -3.320 -1.488 0.930
-0.380 -0.755 -0.592 -0.849 -0.712 0.474 0.471 -0.345 -0.139 -0.214 0.633 -0.146 1.397 -0.101 0.368 -0.772 -0.287 0.843 -0.498 -0.002 0.429 -0.617 0.808 -0.230 0.515 1.515 -0.120 0.236 -0.056 0.035 0.283 0.323
-0.439 1.435 -1.342 0.747 0.912 -0.001 -0.784 0.359 0.029 -0.044 -0.785 -1.087 -0.520 0.080 1.190 1.515 1.300 -0.745 0.375 0.559 -1.703 0.001 0.320 0.197 -0.133 0.538 -0.838 -0.530 0.348 -0.465 -0.156 0.373
-0.273 -0.624 1.047 0.652 -0.459 -0.065 -1.362 0.395 -0.268 0.184 -0.490 -0.998 0.141 -0.064 -1.307 -0.629 -1.420 -0.311 0.138 0.128 -0.578 0.022 -0.189 0.813 -0.371 0.518 0.127 -0.633 0.147 -0.063 -0.063 0.649
-0.182 0.414 1.113 1.300 -0.555 -1.023 -0.273 0.255 1.854 0.894 -1.754 0.874 -1.174 1.000 0.014 -0.565 -0.245 -1.460 1.908 0.427 -0.234 1.958 -0.381 0.342 -1.569 -0.259 1.498 -1.148 0.707 0.411 -0.962 0.428
-0.702 1.184 -0.770 0.018 1.339 0.650 0.331 -0.324 -0.754 -0.974 -0.545 0.947 0.426 -0.906 1.191 1.322 -0.960 -0.784 1.133 -0.219 -0.192 -0.154 1.212 -0.185 0.867 0.586 0.269 0.202 -0.499 -0.108 0.563 -0.102
0.123 1.131 0.253 -0.249 0.528 0.671 0.727 0.373 -0.519 0.188 -1.687 0.282 -0.699 0.284 0.409 1.741 0.001 -1.785 1.594 1.352 0.164 -0.001 -0.703 0.233 1.169 0.059 1.647 -0.160 -1.172 0.296 0.186 1.657
1.028 0.924 0.862 -0.293 -0.780 -0.338 -1.342 -0.086 0.311 0.028 0.308 -0.146 0.985 -0.963 0.429 0.500 -0.556 0.462 -0.400 -0.035 1.260 0.238 -0.439 -0.847 0.355 1.918 0.222 -1.080 -0.183 -0.088 -0.340 0.634
-2.245 0.076 -1.065 -0.448 -0.255 -0.646 1.464 1.854 -1.087 -0.166 0.822 0.612 -2.074 0.742 -0.615 -0.737 -1.722 1.021 -1.255 -0.978 0.260 -0.047 -1.030 1.524 0.396 -0.414 -0.505 0.920 1.139 0.654 -0.747 -1.305
-0.392 -1.434 -1.017 -0.670 -0.501 2.027 0.308 0.018 -0.128 -1.482 2.459 -0.283 -0.269 -1.611 -0.260 -0.369 0.520 2.611 -2.243 -0.754 0.561 -0.558 0.996 0.175 0.662 1.374 -1.635 0.057 -1.977 1.748 1.522 -1.755
0.199 -0.628 0.006 1.892 0.399 1.301 -2.293 -2.039 0.245 -1.276 -1.004 -0.728 0.824 -1.192 -0.055 -0.449 0.540 -0.869 0.627 0.544 -0.054 1.279 1.204 -0.044 -2.014 -0.079 0.774 -0.583 -1.493 -2.010 1.421 0.597
1.128 0.176 1.452 0.363 0.484 0.567 -0.330 -1.748 -0.600 -0.011 -0.322 -0.452 1.395 -0.888 0.200 0.143 -0.360 -0.698 0.127 0.668 0.372 0.455 0.731 -0.868 0.025 0.858 0.035 0.447 -0.104 -0.864 0.888 0.358
-0.055 -0.592 -1.659 -1.811 0.209 1.135 0.388 -0.099 -2.827 -1.141 0.964 -1.547 0.131 0.050 -1.376 -0.311 -0.062 1.004 -0.812 -1.031 -0.478 -1.838 0.846 0.749 1.954 0.627 -0.076 1.829 -0.651 -0.273 1.134 -0.102
0.302 0.186 -0.727 -1.590 0.882 3.504 0.386 -0.018 0.057 -3.746 2.285 -2.535 -0.123 -0.926 -0.664 0.775 -0.775 2.517 -1.358 -0.835 0.776 -1.583 1.810 1.678 0.301 -0.297 -0.972 0.810 -2.906 2.699 2.761 -0.531
0.301 -0.212 -0.929 -0.722 0.566 0.114 1.187 0.209 -1.292 -0.270 1.556 -0.081 -0.037 0.880 -1.587 0.118 -0.764 1.513 -0.596 -1.049 -0.788 -0.759 0.270 2.222 1.393 -0.085 -1.305 0.655 -0.264 1.076 0.014 -1.823
1.078 0.154 1.176 -0.170 -0.396 -0.176 0.191 -0.514 0.117 0.079 0.118 0.284 0.235 -0.836 0.192 0.486 -0.557 0.256 -0.282 0.830 -0.367 -0.656 -0.381 -0.562 0.361 0.913 0.262 -1.743 -0.005 0.206 -0.344 0.584
0.551 -0.620 -0.033 -1.499 -0.410 1.771 0.317 -0.159 -0.056 -2.324 1.383 -1.284 0.565 -0.584 -0.850 -0.690 0.346 1.410 -1.055 0.204 0.085 -1.209 2.017 0.634 0.728 0.145 -0.661 0.684 -2.035 0.192 1.550 -1.204
-0.173 -0.926 -0.307 0.213 0.119 0.948 -0.472 -1.267 0.122 -0.909 -0.254 -0.077 0.256 0.331 -0.225 -0.493 -0.900 0.256 0.337 0.666 0.742 0.200 0.183 -0.251 0.196 0.140 0.278 -0.132 -0.274 0.059 0.255 0.202
-0.068 -0.752 0.388 0.016 -0.235 -0.164 0.056 -0.658 0.174 -0.237 0.334 0.771 0.204 0.170 -0.006 -0.681 -0.797 0.242 0.056 0.220 0.445 -0.687 -0.542 -0.379 0.377 -0.055 0.234 -0.164 -0.323 -0.392 0.230 0.331
0.360 0.423 0.233 0.447 -0.333 -1.231 -0.185 1.190 0.643 1.005 -1.012 0.449 -1.260 0.682 0.318 0.661 -0.364 -1.236 0.433 -0.338 0.017 0.436 -0.556 0.365 -0.480 -0.088 0.472 0.057 0.821 -1.140 -1.209 0.251
0.359 -0.379 0.626 -0.245 0.007 0.628 0.051 -0.286 -0.336 -0.470 0.265 0.262 -0.033 0.445 -0.531 -0.317 -0.656 0.051 -0.195 0.060 -0.705 -0.368 -0.263 -0.850 0.567 0.323 -0.499 0.260 -0.611 -0.412 0.317 -0.206
0.238 0.845 -0.676 -0.262 -0.432 1.674 1.221 0.173 -0.830 -1.928 2.269 -0.683 0.390 0.047 -0.384 -0.371 -0.613 2.262 -2.333 -1.433 0.536 -0.219 0.655 1.063 1.370 -0.322 -2.262 1.224 -1.766 -0.240 0.867 -1.000
2.131 -1.703 -0.196 -1.025 -3.398 0.313 -0.550 -1.003 0.121 -0.192 1.179 0.656 1.897 0.098 -1.964 -1.706 -2.436 1.126 0.251 1.044 2.016 -0.791 1.355 0.314 0.638 2.277 0.107 -0.356 -0.373 0.191 0.066 -0.327

b2 (layer 2 biases), shape 32:
-0.188 0.590 -0.599 -0.117 -0.049 -0.752 -0.100 0.011 -0.017 0.578 0.355 -0.551 0.160 -0.165 -0.166 0.312 -0.076 0.381 -0.299 -0.778 0.022 0.199 0.778 0.538 -0.287 0.226 -0.220 0.308 0.540 0.605 -0.240 -0.546

W3 (output layer weights), shape 32 x 5: one line per row
-1.025 -2.129 -0.508 -1.733 0.078
1.502 1.718 1.370 1.597 -1.796
-0.068 -1.333 -1.384 -0.441 -2.100
-0.317 -0.054 -1.068 -0.403 -3.063
1.127 0.094 1.375 3.081 0.115
-1.616 -1.409 -1.560 -1.293 1.419
1.035 0.850 0.779 0.931 1.518
0.621 1.241 -0.291 2.324 1.378
-1.090 -0.159 -0.928 -1.629 -2.009
1.179 1.049 1.268 1.659 -0.327
1.460 1.354 1.328 -0.903 1.549
-0.177 0.185 0.450 4.057 -0.414
-0.286 0.283 -1.528 -2.727 -1.200
0.884 1.602 1.334 1.398 -0.152
0.946 0.880 0.802 2.492 -1.654
1.084 1.177 1.580 2.754 -1.673
0.462 1.013 0.549 2.570 1.113
1.534 1.461 1.447 -1.106 1.391
-1.270 -1.182 -1.382 0.462 -1.604
-1.047 -1.294 -1.043 -0.477 -1.395
-0.782 -0.989 -1.499 -1.514 -0.673
-0.570 -0.832 0.144 -1.188 -2.949
0.376 0.387 0.537 -5.336 -1.203
1.539 0.781 0.840 0.460 1.692
0.367 0.561 0.305 0.911 3.031
-1.960 -0.579 -0.441 -1.476 -0.984
-0.683 -1.201 -1.744 -0.215 -1.358
0.607 1.394 0.809 0.496 1.190
1.547 1.395 1.382 1.417 -1.013
1.535 1.638 1.406 -1.549 1.526
-1.645 -1.382 -1.458 -1.346 1.380
-1.264 -1.051 -1.013 -0.268 -1.647

b3 (output layer biases), shape 5:
1.221 1.008 1.024 0.897 1.298