diff --git a/POET_Training.ipynb b/POET_Training.ipynb index 5f51402..12cc085 100644 --- a/POET_Training.ipynb +++ b/POET_Training.ipynb @@ -27,9 +27,18 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-15 11:56:01.330345: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-01-15 11:56:01.443139: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -62,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -196,17 +205,17 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Model: \"sequential_4\"\n",
+       "
Model: \"sequential_1\"\n",
        "
\n" ], "text/plain": [ - "\u001b[1mModel: \"sequential_4\"\u001b[0m\n" + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" ] }, "metadata": {}, @@ -218,13 +227,13 @@ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                     Output Shape                  Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_15 (Dense)                │ (None, 512)            │         6,656 │\n",
+       "│ dense_3 (Dense)                 │ (None, 512)            │         6,656 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_16 (Dense)                │ (None, 1024)           │       525,312 │\n",
+       "│ dense_4 (Dense)                 │ (None, 1024)           │       525,312 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_17 (Dense)                │ (None, 512)            │       524,800 │\n",
+       "│ dense_5 (Dense)                 │ (None, 512)            │       524,800 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_18 (Dense)                │ (None, 12)             │         6,156 │\n",
+       "│ dense_6 (Dense)                 │ (None, 12)             │         6,156 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n" ], @@ -232,13 +241,13 @@ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ dense_15 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m6,656\u001b[0m │\n", + "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m6,656\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_16 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m525,312\u001b[0m │\n", + "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m525,312\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_17 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,800\u001b[0m │\n", + "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,800\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_18 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m6,156\u001b[0m │\n", + "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m6,156\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, @@ -377,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [