Edit keras export notebook (#1007)

Add usage example of keras_load_quantized_model.
Change paths to local paths.
Remove compression ratio calculation in int8.

Co-authored-by: reuvenp <reuvenp@altair-semi.com>

tutorials/notebooks/keras/export/example_keras_export.ipynb

@@ -47,11 +47,11 @@
       "cell_type": "code",
       "source": [
         "import numpy as np\n",
-        "from keras.applications import ResNet50\n",
+        "from keras.applications import MobileNetV2\n",
         "import model_compression_toolkit as mct\n",
         "\n",
         "# Create a model\n",
-        "float_model = ResNet50()\n",
+        "float_model = MobileNetV2()\n",
         "# Quantize the model.\n",
         "# Notice that here the representative dataset is random for demonstration only.\n",
         "quantized_exportable_model, _ = mct.ptq.keras_post_training_quantization(float_model,\n",
@@ -87,10 +87,8 @@
     {
       "cell_type": "code",
       "source": [
-        "import tempfile\n",
-        "\n",
         "# Path of exported model\n",
-        "_, keras_file_path = tempfile.mkstemp('.keras')\n",
+        "keras_file_path = 'exported_model_mctq.keras'\n",
         "\n",
         "# Export a keras model with mctq custom quantizers.\n",
         "mct.exporter.keras_export_model(model=quantized_exportable_model,\n",
@@ -107,17 +105,40 @@
       "source": [
         "Notice that the model has the same size as the quantized exportable model as weights data types are float.\n",
         "\n",
-        "#### Fakely-Quantized"
+        "#### MCTQ - Loading Exported Model\n",
+        "\n",
+        "To load the exported model with MCTQ quantizers, use `mct.keras_load_quantized_model`:"
       ],
       "metadata": {
         "id": "Bwx5rxXDF_gb"
       }
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "loaded_model = mct.keras_load_quantized_model(keras_file_path)"
+      ],
+      "metadata": {
+        "id": "q235XNJQmTdd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "#### Fakely-Quantized"
+      ],
+      "metadata": {
+        "id": "sOmDjSehlQba"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
         "# Path of exported model\n",
-        "_, keras_file_path = tempfile.mkstemp('.keras')\n",
+        "keras_file_path = 'exported_model_fakequant.keras'\n",
         "\n",
         "# Use mode KerasExportSerializationFormat.KERAS for a .keras model\n",
         "# and QuantizationFormat.FAKELY_QUANT for fakely-quantized weights\n",
@@ -154,10 +175,7 @@
     {
       "cell_type": "code",
       "source": [
-        "import tempfile\n",
-        "\n",
-        "# Path of exported model\n",
-        "_, tflite_file_path = tempfile.mkstemp('.tflite')\n",
+        "tflite_file_path = 'exported_model_int8.tflite'\n",
         "\n",
         "# Use mode KerasExportSerializationFormat.TFLITE for tflite model and quantization_format.INT8.\n",
         "mct.exporter.keras_export_model(model=quantized_exportable_model,\n",
@@ -186,12 +204,11 @@
         "import os\n",
         "\n",
         "# Save float model to measure its size\n",
-        "_, float_file_path = tempfile.mkstemp('.keras')\n",
+        "float_file_path = 'exported_model_float.keras'\n",
         "float_model.save(float_file_path)\n",
         "\n",
         "print(\"Float model in Mb:\", os.path.getsize(float_file_path) / float(2 ** 20))\n",
-        "print(\"Quantized model in Mb:\", os.path.getsize(tflite_file_path) / float(2 ** 20))\n",
-        "print(f'Compression ratio: {os.path.getsize(float_file_path) / os.path.getsize(tflite_file_path)}')"
+        "print(\"Quantized model in Mb:\", os.path.getsize(tflite_file_path) / float(2 ** 20))"
       ],
       "metadata": {
         "id": "LInM16OMGUtF"
@@ -217,7 +234,8 @@
       "cell_type": "code",
       "source": [
         "# Path of exported model\n",
-        "_, tflite_file_path = tempfile.mkstemp('.tflite')\n",
+        "tflite_file_path = 'exported_model_fakequant.tflite'\n",
+        "\n",
         "\n",
         "# Use mode KerasExportSerializationFormat.TFLITE for tflite model and QuantizationFormat.FAKELY_QUANT for fakely-quantized weights\n",
         "# and activations.\n",