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nielsrolf/AlgorithmLearningByGradientDescent.ipynb

Created November 17, 2021 17:14
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Neural networks for logical tasks, but the weights are probabilities for the effective weight to be either 1 or 0 in the forward pass
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AlgorithmLearningByGradientDescent.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "_Lpky9zjCQI1"
},
"source": [
"# Can a neural network learn to use calculator?\n",
"\n",
"In this notebook, I want so evaluate a simple idea: if we add untrainable layers that perform logical operations, can a neural network learn to use them in order to solve logical tasks?\n",
"\n",
"Layers:\n",
"- Binary AND\n",
"- Binary OR\n",
"- Binary XOR\n",
"- Binary ADD\n",
"- Binary MOD\n",
"- Integer to binary\n",
"\n",
"I test this question by training a network on synthethic algorithmic data like in the grokking paper, where the network has to use multiple logical operations in the correct order to solve a task.\n",
"\n",
"I hypothize that neuron activations can be seen as logits for the probability that some feature is present and therefore implement logical operations that receive logits and output logits\n",
"\n",
"Data:\n",
"As the task, we define binary operators and then pass strings like\n",
"`x o y` as input. Later, we can also use `((x o y) o (z o a))` as task\n",
"\n",
"Other ideas:\n",
"- Use binary weights in order to really learn algorithms:\n",
"```\n",
"def forward(w, x, f):\n",
" z = tf.random.uniform(0, 1, w.shape)\n",
" w_binary = w > z\n",
" w_binary_with_gradient = w_binary + w - tf.stop_gradient(w)\n",
" return f(w_binary_with_gradient, x, f)\n",
"```\n",
"- Use this in a PonderNet style with nested equations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "lxMAM8fIEiAq"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"\n",
"def softmax(x, axis):\n",
" z = x - tf.reduce_max(x, axis=axis, keepdims=True)\n",
" numerator = tf.exp(z)\n",
" denominator = tf.reduce_sum(numerator, axis=axis, keepdims=True)\n",
" return tf.stop_gradient(numerator / denominator)\n",
"\n",
"\n",
"def OR(x, axis):\n",
" \"\"\"\n",
" P(z) = max(P(x), p(y))\n",
" logit(z) = P(x > y) * x + P(y > z) * y\n",
" \"\"\"\n",
" px = softmax(x, axis)\n",
" return tf.reduce_sum(px * x, axis=axis)\n",
"\n",
"\n",
"def AND(x, axis):\n",
" \"\"\"\n",
" P(z) = min(P(x), P(y))\n",
" \"\"\"\n",
" return 1 - OR(-x, axis)\n",
"\n",
"\n",
"\n",
"def XOR(x, axis):\n",
" \"\"\"\n",
" P(z) = max(P(x), p(y)) - min(P(x), P(y))\n",
" \"\"\"\n",
" return OR(x, axis) - AND(x, axis)\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JL0Mx8JMjTUG"
},
"source": [
"# Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5nNKcTtBjSYs",
"outputId": "63998563-9dc3-410c-aad8-6db07c55b8c9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 o 0 = 0\n",
"0 o 1 = 0\n",
"1 o 1 = 1\n",
"0 o 0 = 0\n",
"3 o 2 = 2\n",
"2 o 1 = 2\n",
"2 o 3 = 2\n",
"1 o 2 = 2\n",
"3 o 3 = 1\n",
"0 o 3 = 0\n",
"0 o 2 = 0\n",
"3 o 1 = 3\n",
"2 o 0 = 0\n",
"1 o 0 = 0\n",
"1 o 3 = 3\n",
"2 o 2 = 0\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"def create_simple_dataset(f, num_symbols, shuffle=True):\n",
" \"\"\"Create the full dataset\n",
" \n",
" Returns:\n",
" A: (batch, num_symbols)\n",
" B: (batch, num_symbols)\n",
" C: (batch, num_symbols)\n",
" \n",
" such that f(a, b) = c for a, b, c being rows of A, B, C\n",
" \"\"\"\n",
" words = np.eye(num_symbols)\n",
" A, B, C = [], [], []\n",
" for a in range(num_symbols):\n",
" for b in range(num_symbols):\n",
" c = f(a, b)\n",
" A += [words[a]]\n",
" B += [words[b]]\n",
" C += [words[c]]\n",
"\n",
" A = np.array(A)\n",
" B = np.array(B)\n",
" C = np.array(C)\n",
"\n",
" if shuffle:\n",
" perm = np.random.permutation(len(A))\n",
" A = A[perm]\n",
" B = B[perm]\n",
" C = C[perm]\n",
"\n",
" return tf.stack(A), tf.stack(B), tf.stack(C)\n",
"\n",
"\n",
"def to_symbol(x):\n",
" \"\"\"\n",
" x: (batch, num_symbols)\n",
" \"\"\"\n",
" return tf.argmax(x, axis=1).numpy()\n",
"\n",
"\n",
"\n",
"def op1(a, b):\n",
" return (a * b) % 4\n",
"\n",
"\n",
"A, B, C = create_simple_dataset(op1, 4)\n",
"\n",
"\n",
"def print_simple_dataset(A, B, C):\n",
" s_a = to_symbol(A)\n",
" s_b = to_symbol(B)\n",
" s_c = to_symbol(C)\n",
" for i in range(len(A)):\n",
" print(int(s_a[i]), \"o\", int(s_b[i]), \"=\", int(s_c[i]))\n",
"\n",
"\n",
"print_simple_dataset(A, B, C)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "rUajLi94g5NC"
},
"outputs": [],
"source": [
"def op1(a, b):\n",
" return (a * b) % 128\n",
"\n",
"\n",
"A, B, C = create_simple_dataset(op1, 128)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yx9eCF0Xhn-T",
"outputId": "80aea6ef-a741-49ab-d432-9537bc995ebb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"112 o 15 = 16\n",
"71 o 93 = 75\n",
"72 o 110 = 112\n",
"10 o 86 = 92\n",
"110 o 13 = 22\n",
"37 o 105 = 45\n",
"3 o 60 = 52\n",
"11 o 59 = 9\n",
"87 o 79 = 89\n",
"105 o 30 = 78\n"
]
}
],
"source": [
"train_frac = 0.5\n",
"\n",
"train_samples = int(train_frac * len(A))\n",
"A_train, A_test = A[:train_samples], A[train_samples:]\n",
"B_train, B_test = B[:train_samples], B[train_samples:]\n",
"C_train, C_test = C[:train_samples], C[train_samples:]\n",
"\n",
"\n",
"print_simple_dataset(A_train[:10], B_train[:10], C_train[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OFFkFMG5jvBU"
},
"source": [
"# Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "q7IUoOmrpD3q",
"outputId": "2f380997-5ef7-4aa0-f6eb-cab0469c5988"
},
"outputs": [
{
"data": {
"text/plain": [
"TensorShape([8192, 128])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"C_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LoY4pAXVjuny",
"outputId": "e93ed70a-2107-4edb-c5f3-35a34b9febc9",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train: 179.74237 Test: 58.53147 Test Acc: 0.0096435546875\n",
"Train: 65.18155 Test: 46.051693 Test Acc: 0.0146484375\n",
"Train: 54.97684 Test: 42.681507 Test Acc: 0.0162353515625\n",
"Train: 51.590385 Test: 35.906296 Test Acc: 0.0150146484375\n",
"Train: 45.028545 Test: 31.140965 Test Acc: 0.0111083984375\n",
"Train: 41.647797 Test: 29.699448 Test Acc: 0.013427734375\n",
"Train: 37.68882 Test: 26.225058 Test Acc: 0.013427734375\n",
"Train: 36.317375 Test: 25.561699 Test Acc: 0.0179443359375\n",
"Train: 33.072407 Test: 23.651585 Test Acc: 0.014404296875\n",
"Train: 30.158392 Test: 20.82552 Test Acc: 0.0111083984375\n",
"Train: 28.734509 Test: 19.138063 Test Acc: 0.0111083984375\n",
"Train: 27.195948 Test: 20.18237 Test Acc: 0.009765625\n",
"Train: 25.710865 Test: 16.710312 Test Acc: 0.012939453125\n",
"Train: 23.977818 Test: 15.749345 Test Acc: 0.0125732421875\n",
"Train: 22.873508 Test: 14.685822 Test Acc: 0.0107421875\n",
"Train: 21.404718 Test: 13.734611 Test Acc: 0.010986328125\n",
"Train: 20.325542 Test: 12.667313 Test Acc: 0.0107421875\n",
"Train: 18.79334 Test: 11.9779005 Test Acc: 0.01123046875\n",
"Train: 17.966013 Test: 11.106178 Test Acc: 0.00927734375\n",
"Train: 17.421747 Test: 10.702664 Test Acc: 0.01220703125\n",
"Train: 16.414906 Test: 10.012283 Test Acc: 0.0096435546875\n",
"Train: 15.078797 Test: 9.392868 Test Acc: 0.00927734375\n",
"Train: 14.226055 Test: 8.997482 Test Acc: 0.0093994140625\n",
"Train: 13.709461 Test: 8.294837 Test Acc: 0.011474609375\n",
"Train: 12.993277 Test: 8.649572 Test Acc: 0.009521484375\n",
"Train: 12.466752 Test: 7.450693 Test Acc: 0.0093994140625\n",
"Train: 11.870171 Test: 6.952836 Test Acc: 0.0103759765625\n",
"Train: 10.994676 Test: 6.7745237 Test Acc: 0.0107421875\n",
"Train: 10.723835 Test: 6.5263796 Test Acc: 0.0081787109375\n",
"Train: 9.883624 Test: 6.5826263 Test Acc: 0.009521484375\n",
"Train: 9.737316 Test: 6.1134787 Test Acc: 0.010009765625\n",
"Train: 9.009263 Test: 5.656737 Test Acc: 0.009765625\n",
"Train: 8.700807 Test: 5.5713034 Test Acc: 0.009521484375\n",
"Train: 8.399522 Test: 5.3454094 Test Acc: 0.0123291015625\n",
"Train: 8.039874 Test: 5.274467 Test Acc: 0.01220703125\n",
"Train: 7.501814 Test: 5.199665 Test Acc: 0.0123291015625\n",
"Train: 7.1898136 Test: 5.1193085 Test Acc: 0.015625\n",
"Train: 6.966585 Test: 5.025529 Test Acc: 0.0155029296875\n",
"Train: 6.8201733 Test: 4.995767 Test Acc: 0.0115966796875\n",
"Train: 6.667398 Test: 4.9313517 Test Acc: 0.0159912109375\n",
"Train: 6.363597 Test: 4.9387527 Test Acc: 0.0140380859375\n",
"Train: 6.180231 Test: 4.8664284 Test Acc: 0.0164794921875\n",
"Train: 5.9685035 Test: 4.8466783 Test Acc: 0.01416015625\n",
"Train: 5.7775545 Test: 4.7962775 Test Acc: 0.0155029296875\n",
"Train: 5.7451277 Test: 4.788171 Test Acc: 0.014892578125\n",
"Train: 5.518394 Test: 4.762039 Test Acc: 0.0159912109375\n",
"Train: 5.3891106 Test: 4.70523 Test Acc: 0.0166015625\n",
"Train: 5.29036 Test: 4.708751 Test Acc: 0.0189208984375\n",
"Train: 5.1856623 Test: 4.66337 Test Acc: 0.018798828125\n",
"Train: 5.128566 Test: 4.6625977 Test Acc: 0.0167236328125\n",
"Train: 4.9597692 Test: 4.6488028 Test Acc: 0.01806640625\n",
"Train: 4.982988 Test: 4.670578 Test Acc: 0.01513671875\n",
"Train: 4.8544965 Test: 4.6464086 Test Acc: 0.0164794921875\n",
"Train: 4.7675323 Test: 4.6249094 Test Acc: 0.017578125\n",
"Train: 4.751253 Test: 4.611937 Test Acc: 0.0203857421875\n",
"Train: 4.6926937 Test: 4.6367073 Test Acc: 0.0152587890625\n",
"Train: 4.624979 Test: 4.577386 Test Acc: 0.0220947265625\n",
"Train: 4.5891566 Test: 4.623365 Test Acc: 0.0155029296875\n",
"Train: 4.599628 Test: 4.6575723 Test Acc: 0.0238037109375\n",
"Train: 4.504484 Test: 4.6841836 Test Acc: 0.013427734375\n",
"Train: 4.4578714 Test: 4.689317 Test Acc: 0.013427734375\n",
"Train: 4.4246864 Test: 4.715329 Test Acc: 0.01708984375\n",
"Train: 4.38454 Test: 4.699116 Test Acc: 0.0166015625\n",
"Train: 4.350828 Test: 4.75238 Test Acc: 0.01171875\n",
"Train: 4.3393264 Test: 4.666083 Test Acc: 0.0159912109375\n",
"Train: 4.250307 Test: 4.7049923 Test Acc: 0.01416015625\n",
"Train: 4.243422 Test: 4.74805 Test Acc: 0.01123046875\n",
"Train: 4.201667 Test: 4.725277 Test Acc: 0.0118408203125\n",
"Train: 4.204354 Test: 4.74037 Test Acc: 0.0120849609375\n",
"Train: 4.195368 Test: 4.744556 Test Acc: 0.0123291015625\n",
"Train: 4.169717 Test: 4.7356377 Test Acc: 0.013427734375\n",
"Train: 4.1408753 Test: 4.8153725 Test Acc: 0.0120849609375\n",
"Train: 4.1675572 Test: 4.812936 Test Acc: 0.0123291015625\n",
"Train: 4.137606 Test: 4.850551 Test Acc: 0.0106201171875\n",
"Train: 4.076514 Test: 4.807287 Test Acc: 0.0118408203125\n",
"Train: 4.067353 Test: 4.829844 Test Acc: 0.012451171875\n",
"Train: 4.0298157 Test: 4.7807837 Test Acc: 0.0133056640625\n",
"Train: 3.980886 Test: 4.6863146 Test Acc: 0.0128173828125\n",
"Train: 4.013456 Test: 4.681673 Test Acc: 0.01318359375\n",
"Train: 3.9690392 Test: 4.759571 Test Acc: 0.013916015625\n",
"Train: 3.9823453 Test: 4.776622 Test Acc: 0.010009765625\n",
"Train: 3.9520001 Test: 4.7796907 Test Acc: 0.0133056640625\n",
"Train: 3.9645495 Test: 4.852233 Test Acc: 0.011474609375\n",
"Train: 3.9456515 Test: 4.814711 Test Acc: 0.0107421875\n",
"Train: 3.9034681 Test: 4.842863 Test Acc: 0.0111083984375\n",
"Train: 3.9337919 Test: 4.898119 Test Acc: 0.0106201171875\n",
"Train: 3.8974106 Test: 4.9008846 Test Acc: 0.0128173828125\n",
"Train: 3.8731396 Test: 4.976475 Test Acc: 0.011962890625\n",
"Train: 3.8437066 Test: 5.0236597 Test Acc: 0.01123046875\n",
"Train: 3.812923 Test: 5.072201 Test Acc: 0.01171875\n",
"Train: 3.764308 Test: 5.1092424 Test Acc: 0.01123046875\n",
"Train: 3.743652 Test: 5.2035627 Test Acc: 0.011962890625\n",
"Train: 3.7540066 Test: 5.1725993 Test Acc: 0.0142822265625\n",
"Train: 3.7806182 Test: 4.9042463 Test Acc: 0.01708984375\n",
"Train: 3.6928856 Test: 4.9411297 Test Acc: 0.013916015625\n",
"Train: 3.6567307 Test: 4.9550934 Test Acc: 0.021484375\n",
"Train: 3.619433 Test: 4.954277 Test Acc: 0.0142822265625\n",
"Train: 3.6107223 Test: 5.0897565 Test Acc: 0.0191650390625\n",
"Train: 3.581727 Test: 5.1699862 Test Acc: 0.0235595703125\n",
"Train: 3.5699859 Test: 5.1484537 Test Acc: 0.0172119140625\n",
"Train: 3.5568087 Test: 5.005471 Test Acc: 0.016845703125\n",
"Train: 3.547943 Test: 4.9921765 Test Acc: 0.018798828125\n",
"Train: 3.5468063 Test: 5.1340566 Test Acc: 0.0272216796875\n",
"Train: 3.5143464 Test: 5.174181 Test Acc: 0.0274658203125\n",
"Train: 3.5384812 Test: 5.3433146 Test Acc: 0.0260009765625\n",
"Train: 3.4666471 Test: 5.106758 Test Acc: 0.0113525390625\n",
"Train: 3.5605383 Test: 5.3417 Test Acc: 0.022705078125\n",
"Train: 3.4586704 Test: 5.2055225 Test Acc: 0.0191650390625\n",
"Train: 3.4223578 Test: 5.317775 Test Acc: 0.0223388671875\n",
"Train: 3.4526587 Test: 5.498246 Test Acc: 0.0150146484375\n",
"Train: 3.407544 Test: 5.368846 Test Acc: 0.022216796875\n",
"Train: 3.3883572 Test: 6.0190573 Test Acc: 0.0172119140625\n",
"Train: 3.384394 Test: 5.6000357 Test Acc: 0.02197265625\n",
"Train: 3.3730907 Test: 5.7587185 Test Acc: 0.0147705078125\n",
"Train: 3.3598957 Test: 5.242864 Test Acc: 0.0255126953125\n",
"Train: 3.3401017 Test: 5.5297766 Test Acc: 0.02783203125\n",
"Train: 3.3373213 Test: 5.50721 Test Acc: 0.0184326171875\n",
"Train: 3.349691 Test: 5.379261 Test Acc: 0.030029296875\n",
"Train: 3.3445547 Test: 5.07484 Test Acc: 0.0277099609375\n",
"Train: 3.2887378 Test: 5.3171344 Test Acc: 0.0294189453125\n",
"Train: 3.302498 Test: 5.300196 Test Acc: 0.029296875\n",
"Train: 3.3328705 Test: 5.5558577 Test Acc: 0.0272216796875\n",
"Train: 3.3048322 Test: 5.6604166 Test Acc: 0.0277099609375\n",
"Train: 3.2742355 Test: 5.750196 Test Acc: 0.0289306640625\n",
"Train: 3.298884 Test: 5.670967 Test Acc: 0.0274658203125\n",
"Train: 3.309151 Test: 5.820493 Test Acc: 0.02685546875\n",
"Train: 3.2554667 Test: 5.7306366 Test Acc: 0.027099609375\n",
"Train: 3.2769866 Test: 5.681945 Test Acc: 0.027587890625\n",
"Train: 3.2629888 Test: 5.9127517 Test Acc: 0.027587890625\n",
"Train: 3.2923226 Test: 6.1234627 Test Acc: 0.0272216796875\n",
"Train: 3.2434475 Test: 5.89058 Test Acc: 0.0262451171875\n",
"Train: 3.241846 Test: 6.2385283 Test Acc: 0.0284423828125\n",
"Train: 3.219002 Test: 6.1238804 Test Acc: 0.02685546875\n",
"Train: 3.248948 Test: 6.0123487 Test Acc: 0.028076171875\n",
"Train: 3.2378101 Test: 6.160511 Test Acc: 0.0272216796875\n",
"Train: 3.2368963 Test: 5.831768 Test Acc: 0.0286865234375\n",
"Train: 3.2417145 Test: 5.9682546 Test Acc: 0.025634765625\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train: 3.238855 Test: 5.8929706 Test Acc: 0.026123046875\n",
"Train: 3.215577 Test: 5.9093227 Test Acc: 0.0277099609375\n",
"Train: 3.172735 Test: 6.035186 Test Acc: 0.0264892578125\n",
"Train: 3.189354 Test: 6.0111055 Test Acc: 0.0264892578125\n",
"Train: 3.1738904 Test: 6.04936 Test Acc: 0.0274658203125\n",
"Train: 3.159815 Test: 5.8203793 Test Acc: 0.0264892578125\n",
"Train: 3.219638 Test: 6.291342 Test Acc: 0.027099609375\n",
"Train: 3.2033496 Test: 6.148452 Test Acc: 0.0262451171875\n",
"Train: 3.185654 Test: 5.9328585 Test Acc: 0.0206298828125\n",
"Train: 3.1268635 Test: 5.9303927 Test Acc: 0.017578125\n",
"Train: 3.2515085 Test: 6.2197514 Test Acc: 0.0274658203125\n",
"Train: 3.2012823 Test: 5.836518 Test Acc: 0.026611328125\n",
"Train: 3.1485398 Test: 5.7058773 Test Acc: 0.027099609375\n",
"Train: 3.1663198 Test: 5.845483 Test Acc: 0.0277099609375\n",
"Train: 3.1547406 Test: 5.781646 Test Acc: 0.0263671875\n",
"Train: 3.1702077 Test: 6.1316833 Test Acc: 0.02734375\n",
"Train: 3.145896 Test: 6.24282 Test Acc: 0.0118408203125\n",
"Train: 3.1502967 Test: 6.227133 Test Acc: 0.0283203125\n",
"Train: 3.1444046 Test: 6.292472 Test Acc: 0.0289306640625\n",
"Train: 3.1425629 Test: 5.927857 Test Acc: 0.0289306640625\n",
"Train: 3.1415994 Test: 5.8214817 Test Acc: 0.0274658203125\n",
"Train: 3.111322 Test: 6.0139036 Test Acc: 0.0289306640625\n",
"Train: 3.119031 Test: 5.861871 Test Acc: 0.029052734375\n",
"Train: 3.0788927 Test: 6.128703 Test Acc: 0.02685546875\n",
"Train: 3.118042 Test: 6.1139226 Test Acc: 0.026611328125\n",
"Train: 3.1372254 Test: 5.9646635 Test Acc: 0.0263671875\n",
"Train: 3.1193633 Test: 5.6555414 Test Acc: 0.029052734375\n",
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"Train: 2.3016207 Test: 3.3880436 Test Acc: 0.1357421875\n",
"Train: 2.3032835 Test: 3.5802798 Test Acc: 0.1334228515625\n",
"Train: 2.2920027 Test: 3.391039 Test Acc: 0.1314697265625\n",
"Train: 2.3057382 Test: 3.534051 Test Acc: 0.136474609375\n",
"Train: 2.3060675 Test: 3.7258139 Test Acc: 0.130859375\n",
"Train: 2.2942107 Test: 3.4331567 Test Acc: 0.1522216796875\n",
"Train: 2.2880552 Test: 3.4037037 Test Acc: 0.1505126953125\n",
"Train: 2.2951264 Test: 3.5712626 Test Acc: 0.149169921875\n",
"Train: 2.278855 Test: 3.4433258 Test Acc: 0.1444091796875\n",
"Train: 2.314238 Test: 3.3862371 Test Acc: 0.141357421875\n",
"Train: 2.3020957 Test: 3.4309351 Test Acc: 0.1578369140625\n",
"Train: 2.283906 Test: 3.5263631 Test Acc: 0.146240234375\n",
"Train: 2.2893925 Test: 3.5299935 Test Acc: 0.1458740234375\n",
"Train: 2.2960744 Test: 3.4899309 Test Acc: 0.146240234375\n",
"Train: 2.2840383 Test: 3.4748871 Test Acc: 0.1580810546875\n",
"Train: 2.2776196 Test: 3.5223057 Test Acc: 0.150146484375\n",
"Train: 2.288143 Test: 3.2365277 Test Acc: 0.1485595703125\n",
"Train: 2.2950742 Test: 3.5106826 Test Acc: 0.1378173828125\n",
"Train: 2.283754 Test: 3.4205618 Test Acc: 0.1448974609375\n",
"Train: 2.3069808 Test: 3.420206 Test Acc: 0.1513671875\n",
"Train: 2.2776277 Test: 3.5739803 Test Acc: 0.1453857421875\n",
"Train: 2.2853484 Test: 3.402046 Test Acc: 0.1383056640625\n",
"Train: 2.298194 Test: 3.449555 Test Acc: 0.1473388671875\n",
"Train: 2.2940283 Test: 3.5719295 Test Acc: 0.149169921875\n",
"Train: 2.29332 Test: 3.46167 Test Acc: 0.1490478515625\n",
"Train: 2.28309 Test: 3.436762 Test Acc: 0.156005859375\n",
"Train: 2.2907956 Test: 3.33386 Test Acc: 0.1644287109375\n"
]
}
],
"source": [
"tfkl = tf.keras.layers\n",
"\n",
"\n",
"class BinaryDense(tfkl.Layer):\n",
" def __init__(self, dims):\n",
" self.dims = dims\n",
" super().__init__()\n",
"\n",
" def build(self, input_shape):\n",
" '''Create the state of the layer (weights)'''\n",
" # initialize the weights\n",
" w_init = tf.random_uniform_initializer(\n",
" minval=-1, maxval=1, seed=None\n",
" )\n",
" self.w = tf.Variable(name=\"kernel\", initial_value=w_init(shape=(input_shape[-1], self.dims),\n",
" dtype='float32'),trainable=True)\n",
"\n",
" # initialize the biases\n",
" b_init = tf.zeros_initializer()\n",
" self.b = tf.Variable(name=\"bias\",initial_value=b_init(shape=(self.dims,), dtype='float32'),trainable=True)\n",
"\n",
" def call(self, inputs, training):\n",
" '''Defines the computation from inputs to outputs'''\n",
" if training:\n",
" p = tf.random.uniform(self.w.shape, 0, 1)\n",
" else:\n",
" p = 0.5\n",
" w = tf.cast(self.w > p, tf.float32) * 1. + self.w - tf.stop_gradient(self.w)\n",
" return tf.matmul(inputs, w) + self.b\n",
"\n",
"\n",
"class CalcLayer(tfkl.Layer):\n",
" def __init__(self, groups=2, dims=64):\n",
" assert dims % 4 == 0\n",
" dims = dims // 4\n",
" super().__init__()\n",
" self.groups, self.dims = groups, dims\n",
" self.dense_or = BinaryDense(dims*2)\n",
" self.dense_and = BinaryDense(dims*2)\n",
" self.dense_xor = BinaryDense(dims*2)\n",
" self.dense_id = BinaryDense(dims)\n",
" \n",
" def call(self, x, training):\n",
" B, d = x.shape[0], x.shape[1]\n",
"\n",
" x_or = self.dense_or(x, training=training)\n",
" x_or = tf.reshape(x_or, [B, self.dims, 2])\n",
" x_or = OR(x_or, 2)\n",
"\n",
" x_and = self.dense_and(x, training=training)\n",
" x_and = tf.reshape(x_and, [B, self.dims, 2])\n",
" x_and = AND(x_and, 2)\n",
"\n",
" x_xor = self.dense_xor(x, training=training)\n",
" x_xor = tf.reshape(x_xor, [B, self.dims, 2])\n",
" x_xor = XOR(x_xor, 2)\n",
"\n",
" x_id = self.dense_id(x, training=training)\n",
"\n",
" x = tf.concat([x_id, x_or, x_and, x_xor], axis=1)\n",
" return x\n",
"\n",
"\n",
"class ClaculatorUser(tf.keras.Model):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.embed = tfkl.Dense(128)\n",
" self.l1 = CalcLayer()\n",
" self.l2 = CalcLayer()\n",
" self.l3 = CalcLayer()\n",
" self.l4 = CalcLayer()\n",
" self.out = tfkl.Dense(128)\n",
" \n",
" def call(self, A, B, training=True):\n",
" A = self.embed(A)\n",
" B = self.embed(B)\n",
" x = tf.concat([A, B], axis=1)\n",
" x = self.l1(x, training=training)\n",
" x = self.l2(x, training=training)\n",
" x = self.l3(x, training=training)\n",
" x = self.l4(x, training=training)\n",
" x = self.out(x)\n",
" return x\n",
"\n",
"\n",
"\n",
"model = ClaculatorUser()\n",
"optimizer = tf.keras.optimizers.Adam(0.001)\n",
"\n",
"\n",
"A_train = tf.reshape(A_train, [-1, 64, A_train.shape[-1]])\n",
"B_train = tf.reshape(B_train, [-1, 64, B_train.shape[-1]])\n",
"C_train = tf.reshape(C_train, [-1, 64, C_train.shape[-1]])\n",
"num_batches = len(A_train)\n",
"train_losses, test_losses = [], []\n",
"for i in range(100000):\n",
" a, b, c = A_train[i%num_batches], B_train[i%num_batches], C_train[i%num_batches]\n",
" with tf.GradientTape() as tape:\n",
" c_est = model(a, b)\n",
" loss = tf.nn.softmax_cross_entropy_with_logits(labels=c, logits=c_est)\n",
" train_losses += [loss.numpy().mean()]\n",
" grads = tape.gradient(loss, model.trainable_variables)\n",
" optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
" if i % 100 == 0 and i > 0:\n",
" c_est = model(A_test, B_test, training=False)\n",
" test_loss = tf.nn.softmax_cross_entropy_with_logits(labels=C_test, logits=c_est).numpy().mean()\n",
" test_losses += [test_loss]\n",
" test_acc = np.mean(np.argmax(c_est.numpy(), axis=1)==np.argmax(C_test, axis=1))\n",
" print(\"Train: \", np.mean(train_losses[-100:]), \"Test: \", test_loss, \"Test Acc: \", test_acc)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 307
},
"id": "mlxV3F5gjyDJ",
"outputId": "ddfb3e64-e2a9-4805-8cad-e3ccf66ee91e"
},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-07de7f355897>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mA_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mC_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mA_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB_test\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0;31m# Legacy graph support is contained in `training_v1.Model`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1105\u001b[0m \u001b[0mversion_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisallow_legacy_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assert_compile_was_called\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1107\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_call_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1108\u001b[0m \u001b[0m_disallow_inside_tf_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_assert_compile_was_called\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2788\u001b[0m \u001b[0;31m# (i.e. whether the model is built and its inputs/outputs are set).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2789\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_compiled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2790\u001b[0;31m raise RuntimeError('You must compile your model before '\n\u001b[0m\u001b[1;32m 2791\u001b[0m \u001b[0;34m'training/testing. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2792\u001b[0m 'Use `model.compile(optimizer, loss)`.')\n",
"\u001b[0;31mRuntimeError\u001b[0m: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`."
]
}
],
"source": [
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "au_iCN3Ak09o"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train acc: 0.15673828125\n"
]
}
],
"source": [
"A_train_full = tf.reshape(A_train, [-1, 128])\n",
"B_train_full = tf.reshape(B_train, [-1, 128])\n",
"C_train_full = tf.reshape(C_train, [-1, 128])\n",
"c_est = model(A_train_full, B_train_full, training=False)\n",
"train_loss = tf.nn.softmax_cross_entropy_with_logits(labels=C_train_full, logits=c_est).numpy().mean()\n",
"train_acc = np.mean(np.argmax(c_est.numpy(), axis=1)==np.argmax(C_train_full, axis=1))\n",
"print(\"Train acc:\", train_acc)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train: 1.0907936 Test: 1.5534425 Test Acc: 0.3870849609375\n",
"Train: 1.1196197 Test: 1.5431142 Test Acc: 0.3958740234375\n",
"Train: 1.1206871 Test: 1.4957871 Test Acc: 0.4146728515625\n",
"Train: 1.1272767 Test: 1.5915082 Test Acc: 0.3958740234375\n",
"Train: 1.1110739 Test: 1.5854753 Test Acc: 0.3834228515625\n",
"Train: 1.1038669 Test: 1.5375493 Test Acc: 0.3878173828125\n",
"Train: 1.130129 Test: 1.5311286 Test Acc: 0.398193359375\n",
"Train: 1.1106468 Test: 1.5570232 Test Acc: 0.39990234375\n",
"Train: 1.1175895 Test: 1.8294787 Test Acc: 0.3800048828125\n",
"Train: 1.1141386 Test: 1.8403772 Test Acc: 0.3699951171875\n",
"Train: 1.1254884 Test: 1.7356141 Test Acc: 0.39697265625\n",
"Train: 1.1105574 Test: 1.9157152 Test Acc: 0.3839111328125\n",
"Train: 1.1021285 Test: 1.9478431 Test Acc: 0.3724365234375\n",
"Train: 1.1186156 Test: 2.0312855 Test Acc: 0.384765625\n",
"Train: 1.0987102 Test: 2.004223 Test Acc: 0.372314453125\n",
"Train: 1.0957245 Test: 1.7893507 Test Acc: 0.3963623046875\n",
"Train: 1.0961328 Test: 1.932283 Test Acc: 0.386474609375\n",
"Train: 1.0997362 Test: 1.9425886 Test Acc: 0.382080078125\n",
"Train: 1.0854802 Test: 1.8726914 Test Acc: 0.3900146484375\n",
"Train: 1.1014216 Test: 1.8900825 Test Acc: 0.3902587890625\n",
"Train: 1.0962746 Test: 1.9107801 Test Acc: 0.38671875\n",
"Train: 1.1264118 Test: 1.8483037 Test Acc: 0.3955078125\n",
"Train: 1.1037896 Test: 2.0235624 Test Acc: 0.3736572265625\n",
"Train: 1.0931284 Test: 1.9629264 Test Acc: 0.3724365234375\n",
"Train: 1.1004869 Test: 1.9855906 Test Acc: 0.376708984375\n",
"Train: 1.1175917 Test: 1.9247087 Test Acc: 0.3756103515625\n",
"Train: 1.0985668 Test: 2.0083272 Test Acc: 0.3699951171875\n",
"Train: 1.1027961 Test: 1.9873084 Test Acc: 0.3699951171875\n",
"Train: 1.0936108 Test: 1.8646618 Test Acc: 0.3885498046875\n",
"Train: 1.091542 Test: 1.8363544 Test Acc: 0.376708984375\n",
"Train: 1.0935096 Test: 1.7441689 Test Acc: 0.3955078125\n",
"Train: 1.0935398 Test: 1.8054546 Test Acc: 0.38427734375\n",
"Train: 1.085565 Test: 1.9276173 Test Acc: 0.3759765625\n",
"Train: 1.1098149 Test: 1.9554937 Test Acc: 0.360107421875\n",
"Train: 1.1035676 Test: 2.0409179 Test Acc: 0.3712158203125\n",
"Train: 1.0995989 Test: 2.0344481 Test Acc: 0.369873046875\n",
"Train: 1.0944703 Test: 2.056964 Test Acc: 0.3692626953125\n",
"Train: 1.0951821 Test: 2.0425334 Test Acc: 0.3707275390625\n",
"Train: 1.1009308 Test: 1.9190562 Test Acc: 0.378173828125\n",
"Train: 1.1015084 Test: 2.176099 Test Acc: 0.33642578125\n",
"Train: 1.0993499 Test: 2.173044 Test Acc: 0.3353271484375\n",
"Train: 1.0636435 Test: 2.1143188 Test Acc: 0.337890625\n",
"Train: 1.1061698 Test: 2.1234517 Test Acc: 0.338134765625\n",
"Train: 1.0831592 Test: 2.0553756 Test Acc: 0.353271484375\n",
"Train: 1.0843575 Test: 2.1396766 Test Acc: 0.348388671875\n",
"Train: 1.0891422 Test: 2.0864708 Test Acc: 0.3624267578125\n",
"Train: 1.0722997 Test: 2.0479228 Test Acc: 0.3648681640625\n",
"Train: 1.0669626 Test: 2.2147756 Test Acc: 0.3515625\n",
"Train: 1.0804908 Test: 2.1814384 Test Acc: 0.354248046875\n",
"Train: 1.0852842 Test: 1.9259404 Test Acc: 0.379638671875\n",
"Train: 1.085787 Test: 1.8857834 Test Acc: 0.373779296875\n",
"Train: 1.0831317 Test: 1.8128248 Test Acc: 0.380615234375\n",
"Train: 1.0783161 Test: 1.8577516 Test Acc: 0.400634765625\n",
"Train: 1.0830684 Test: 1.815094 Test Acc: 0.398681640625\n",
"Train: 1.0822862 Test: 1.9422972 Test Acc: 0.3795166015625\n",
"Train: 1.0630767 Test: 2.0132966 Test Acc: 0.37890625\n",
"Train: 1.0727443 Test: 2.0402887 Test Acc: 0.37109375\n",
"Train: 1.0798329 Test: 1.9715025 Test Acc: 0.3795166015625\n",
"Train: 1.0795584 Test: 1.9531857 Test Acc: 0.359375\n",
"Train: 1.0724326 Test: 2.0231383 Test Acc: 0.3681640625\n",
"Train: 1.0786409 Test: 1.983747 Test Acc: 0.3759765625\n",
"Train: 1.1015188 Test: 1.9958167 Test Acc: 0.37158203125\n",
"Train: 1.0925431 Test: 1.975081 Test Acc: 0.3668212890625\n",
"Train: 1.07221 Test: 1.9087944 Test Acc: 0.3870849609375\n",
"Train: 1.0635921 Test: 1.9074569 Test Acc: 0.3824462890625\n",
"Train: 1.0702429 Test: 1.8703275 Test Acc: 0.3779296875\n",
"Train: 1.0796101 Test: 2.0235543 Test Acc: 0.37646484375\n",
"Train: 1.0711744 Test: 2.0877588 Test Acc: 0.36572265625\n",
"Train: 1.0652194 Test: 1.9780749 Test Acc: 0.3740234375\n",
"Train: 1.0576037 Test: 2.0323496 Test Acc: 0.368896484375\n",
"Train: 1.0717672 Test: 1.9915862 Test Acc: 0.38037109375\n",
"Train: 1.0784265 Test: 1.945673 Test Acc: 0.372802734375\n",
"Train: 1.0683794 Test: 2.053076 Test Acc: 0.3692626953125\n",
"Train: 1.0509758 Test: 2.038794 Test Acc: 0.3720703125\n",
"Train: 1.0690112 Test: 2.0374978 Test Acc: 0.380615234375\n",
"Train: 1.0754821 Test: 2.104489 Test Acc: 0.3533935546875\n",
"Train: 1.0714489 Test: 2.0395496 Test Acc: 0.3743896484375\n",
"Train: 1.0727303 Test: 2.053781 Test Acc: 0.364501953125\n",
"Train: 1.0650542 Test: 1.8937075 Test Acc: 0.3717041015625\n",
"Train: 1.073499 Test: 1.9122427 Test Acc: 0.37548828125\n",
"Train: 1.0685999 Test: 2.0141473 Test Acc: 0.359375\n",
"Train: 1.0949249 Test: 1.9632881 Test Acc: 0.3599853515625\n",
"Train: 1.0732406 Test: 2.013013 Test Acc: 0.3558349609375\n",
"Train: 1.0629289 Test: 2.0467086 Test Acc: 0.3494873046875\n",
"Train: 1.0584165 Test: 2.0238974 Test Acc: 0.3597412109375\n",
"Train: 1.0659574 Test: 1.945915 Test Acc: 0.3609619140625\n",
"Train: 1.065608 Test: 2.015061 Test Acc: 0.3717041015625\n",
"Train: 1.0529104 Test: 2.0349734 Test Acc: 0.383056640625\n",
"Train: 1.082454 Test: 1.955061 Test Acc: 0.3743896484375\n",
"Train: 1.0823715 Test: 1.9501417 Test Acc: 0.3714599609375\n",
"Train: 1.0695323 Test: 1.918413 Test Acc: 0.380859375\n",
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"Train: 0.13715522 Test: 0.25280666 Test Acc: 0.9752197265625\n",
"Train: 0.101972334 Test: 0.26034707 Test Acc: 0.97412109375\n",
"Train: 0.09506468 Test: 0.24684545 Test Acc: 0.9764404296875\n",
"Train: 0.1116943 Test: 0.24336398 Test Acc: 0.9769287109375\n",
"Train: 0.07889958 Test: 0.26937693 Test Acc: 0.9754638671875\n",
"Train: 0.11107101 Test: 0.2485145 Test Acc: 0.9739990234375\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train: 0.096783735 Test: 0.25350806 Test Acc: 0.9735107421875\n",
"Train: 0.11847043 Test: 0.26153022 Test Acc: 0.9739990234375\n",
"Train: 0.10160412 Test: 0.2568268 Test Acc: 0.9742431640625\n",
"Train: 0.12125275 Test: 0.24798474 Test Acc: 0.9736328125\n",
"Train: 0.08941954 Test: 0.24550268 Test Acc: 0.974609375\n",
"Train: 0.11958702 Test: 0.26124758 Test Acc: 0.9708251953125\n",
"Train: 0.09425978 Test: 0.23647091 Test Acc: 0.97412109375\n",
"Train: 0.09203285 Test: 0.24920863 Test Acc: 0.9735107421875\n",
"Train: 0.108975545 Test: 0.2485232 Test Acc: 0.97412109375\n",
"Train: 0.08440024 Test: 0.23792967 Test Acc: 0.97607421875\n",
"Train: 0.09507236 Test: 0.23827104 Test Acc: 0.9765625\n",
"Train: 0.092582166 Test: 0.23510154 Test Acc: 0.9766845703125\n",
"Train: 0.1026159 Test: 0.23749086 Test Acc: 0.9765625\n",
"Train: 0.11677322 Test: 0.23642072 Test Acc: 0.9764404296875\n",
"Train: 0.08723611 Test: 0.2572701 Test Acc: 0.97021484375\n",
"Train: 0.086944506 Test: 0.25084496 Test Acc: 0.9764404296875\n",
"Train: 0.12276116 Test: 0.23147574 Test Acc: 0.9774169921875\n",
"Train: 0.10222802 Test: 0.23793587 Test Acc: 0.9752197265625\n",
"Train: 0.097563475 Test: 0.24462608 Test Acc: 0.97314453125\n",
"Train: 0.11770766 Test: 0.23716365 Test Acc: 0.9757080078125\n",
"Train: 0.11944092 Test: 0.2365035 Test Acc: 0.9735107421875\n",
"Train: 0.087816864 Test: 0.22867212 Test Acc: 0.9754638671875\n",
"Train: 0.10049224 Test: 0.22140208 Test Acc: 0.9774169921875\n",
"Train: 0.086340755 Test: 0.24167854 Test Acc: 0.970703125\n",
"Train: 0.08358029 Test: 0.22755602 Test Acc: 0.9759521484375\n",
"Train: 0.08132799 Test: 0.21916711 Test Acc: 0.97705078125\n",
"Train: 0.07983109 Test: 0.23641934 Test Acc: 0.9730224609375\n",
"Train: 0.08409298 Test: 0.24207065 Test Acc: 0.971923828125\n"
]
}
],
"source": [
"for i in range(1000000):\n",
" a, b, c = A_train[i%num_batches], B_train[i%num_batches], C_train[i%num_batches]\n",
" with tf.GradientTape() as tape:\n",
" c_est = model(a, b)\n",
" loss = tf.nn.softmax_cross_entropy_with_logits(labels=c, logits=c_est)\n",
" train_losses += [loss.numpy().mean()]\n",
" grads = tape.gradient(loss, model.trainable_variables)\n",
" optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
" if i % 100 == 0 and i > 0:\n",
" c_est = model(A_test, B_test, training=False)\n",
" test_loss = tf.nn.softmax_cross_entropy_with_logits(labels=C_test, logits=c_est).numpy().mean()\n",
" test_losses += [test_loss]\n",
" test_acc = np.mean(np.argmax(c_est.numpy(), axis=1)==np.argmax(C_test, axis=1))\n",
" print(\"Train: \", np.mean(train_losses[-100:]), \"Test: \", test_loss, \"Test Acc: \", test_acc)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorShape([8192, 128])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"C_train_full.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(8192, 128), dtype=float32, numpy=\n",
"array([[-33.43415 , -9.834302 , -3.8031478 , ..., -4.647955 ,\n",
" -4.132407 , -8.422541 ],\n",
" [-20.921886 , -2.0699017 , -1.0960923 , ..., 5.462327 ,\n",
" -1.6415334 , -1.3031597 ],\n",
" [-34.577583 , -9.296862 , -5.557024 , ..., -5.4333477 ,\n",
" -6.00175 , -7.7301474 ],\n",
" ...,\n",
" [-20.24983 , -1.2064404 , -11.447122 , ..., -6.294824 ,\n",
" -11.74326 , -1.0357126 ],\n",
" [-20.88359 , -4.59092 , 7.4503574 , ..., 0.14064594,\n",
" 7.2811413 , -4.50254 ],\n",
" [-20.570114 , -0.6604989 , -10.705523 , ..., -5.9181333 ,\n",
" -11.00922 , -0.51214874]], dtype=float32)>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c_est"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(8192, 128), dtype=float64, numpy=\n",
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]])>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"C_train_full"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 16, 75, 112, ..., 20, 30, 20])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.argmax(C_train_full.numpy(), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([112, 36, 112, ..., 76, 30, 76])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.argmax(c_est.numpy(), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<__main__.ClaculatorUser at 0x28c839820>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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