Typo

notebook.ipynb

@@ -18,7 +18,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 80,
+ "execution_count": 96,
  "metadata": {},
  "outputs": [
  {
@@ -35,7 +35,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 95,
+ "execution_count": 97,
  "metadata": {},
  "outputs": [
  {
@@ -89,7 +89,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 6,
+ "execution_count": 98,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -131,7 +131,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 7,
+ "execution_count": 99,
  "metadata": {},
  "outputs": [
  {
@@ -143,7 +143,7 @@
  " tensor([0, 0, 0, 0, 1, 1], dtype=torch.int32)]"
  ]
  },
- "execution_count": 7,
+ "execution_count": 99,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -161,7 +161,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 69,
+ "execution_count": 100,
  "metadata": {},
  "outputs": [
  {
@@ -198,7 +198,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 88,
+ "execution_count": 101,
  "metadata": {},
  "outputs": [
  {
@@ -458,7 +458,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 89,
+ "execution_count": 102,
  "metadata": {},
  "outputs": [
  {
@@ -500,14 +500,14 @@
  "\n",
  "##### Continuation:\n",
  "For each subsequent sequence, repeat the above iterative learning process. The decompressed sequence from the previous step helps in predicting and decompressing the next.\n",
- "Why This Works:\n",
  "\n",
+ "##### Why This Works:\n",
  "Both encoder and decoder start with identical neural network setups. As sequences are processed, both learn and adjust their weights in the same manner. This synchronized evolution ensures that the decoder can accurately decompress, even without receiving the encoder's neural weights."
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 90,
+ "execution_count": 103,
  "metadata": {},
  "outputs": [
  {
@@ -519,7 +519,7 @@
  " tensor([0, 0, 1], dtype=torch.int32)]"
  ]
  },
- "execution_count": 90,
+ "execution_count": 103,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -538,7 +538,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 91,
+ "execution_count": 104,
  "metadata": {},
  "outputs": [
  {
@@ -552,7 +552,13 @@
  "Input: tensor([[[ 0., 0., -1., -1., -1., -1.]]])\n",
  "Output: [[0.5132045 0.48679546]] (probability distribution))\n",
  "Target: [0], Predicted 0\n",
- "\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
  "Input: tensor([[[ 0., 0., 0., -1., -1., -1.]]])\n",
  "Output: [[0.5253333 0.47466666]] (probability distribution))\n",
  "Target: [0], Predicted 0\n",
@@ -735,16 +741,16 @@
  },
  {
  "cell_type": "code",
- "execution_count": 92,
+ "execution_count": 105,
  "metadata": {},
  "outputs": [
  {
  "name": "stdout",
  "output_type": "stream",
  "text": [
- "-rw-r--r-- 1 maghoi staff 148B Aug 19 12:49 compressed.txt\n",
+ "-rw-r--r-- 1 maghoi staff 148B Aug 19 13:12 compressed.txt\n",
  "-rw-r--r-- 1 maghoi staff 210B Aug 18 23:42 data.txt\n",
- "-rw-r--r-- 1 maghoi staff 210B Aug 19 12:49 decompressed.txt\n"
+ "-rw-r--r-- 1 maghoi staff 210B Aug 19 13:12 decompressed.txt\n"
  ]
  }
  ],
@@ -754,7 +760,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 94,
+ "execution_count": 106,
  "metadata": {},
  "outputs": [
  {
@@ -770,7 +776,7 @@
  "True"
  ]
  },
- "execution_count": 94,
+ "execution_count": 106,
  "metadata": {},
  "output_type": "execute_result"
  }