kiransair/tf_forum_23085.ipynb Secret

## tf_forum_23085.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPVp9woTP8Lp7W1beiD3yHG",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/kiransair/0f0872c64fedd4a77fd6579842f314c1/tf_forum_23085.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PsmJli3xJ_42",
        "outputId": "a3f5871b-90dd-401b-f6f5-40174bb844da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\n"
          ]
        }
      ],
      "source": [
        "import logging\n",
        "logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import pathlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load MNIST dataset\n",
        "mnist = keras.datasets.mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
        "\n",
        "# Normalize the input image so that each pixel value is between 0 to 1.\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0\n",
        "\n",
        "# Define the model architecture\n",
        "model = keras.Sequential([\n",
        "  keras.layers.InputLayer(input_shape=(28, 28)),\n",
        "  keras.layers.Reshape(target_shape=(28, 28, 1)),\n",
        "  keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),\n",
        "  keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "  keras.layers.Flatten(),\n",
        "  keras.layers.Dense(10)\n",
        "])\n",
        "\n",
        "# Train the digit classification model\n",
        "model.compile(optimizer='adam',\n",
        "              loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "model.fit(\n",
        "  train_images,\n",
        "  train_labels,\n",
        "  epochs=1,\n",
        "  validation_data=(test_images, test_labels)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yO70mHplKFve",
        "outputId": "177d2a57-8209-4758-87ae-a0af69bb2f0b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n",
            "1875/1875 [==============================] - 36s 19ms/step - loss: 0.2908 - accuracy: 0.9194 - val_loss: 0.1409 - val_accuracy: 0.9575\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f70b8489c30>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-L_OK9jmc93w",
        "outputId": "ea6aa61d-a2d7-482c-c511-95845b5f029b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpqjec_r4i/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tflite_models_dir = pathlib.Path(\"/tmp/mnist_tflite_models/\")\n",
        "tflite_models_dir.mkdir(exist_ok=True, parents=True)"
      ],
      "metadata": {
        "id": "_jhJrOugdABe"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tflite_model_file = tflite_models_dir/\"mnist_model.tflite\"\n",
        "tflite_model_file.write_bytes(tflite_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6umm3mw4dBmQ",
        "outputId": "90a240cc-0b3e-4150-e5ca-59a04ec6d1f2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "84820"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "tflite_quant_model = converter.convert()\n",
        "tflite_model_quant_file = tflite_models_dir/\"mnist_model_quant.tflite\"\n",
        "tflite_model_quant_file.write_bytes(tflite_quant_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyIS2ls5dDMd",
        "outputId": "458467c8-5eb6-4b4b-d845-c26bc6a702af"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpj2jrid0z/assets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24064"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))\n",
        "interpreter.allocate_tensors()"
      ],
      "metadata": {
        "id": "b-WBeH0udFkk"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interpreter_quant = tf.lite.Interpreter(model_path=str(tflite_model_quant_file))\n",
        "interpreter_quant.allocate_tensors()"
      ],
      "metadata": {
        "id": "jpgmxJVSdHVc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)\n",
        "\n",
        "input_index = interpreter.get_input_details()[0][\"index\"]\n",
        "output_index = interpreter.get_output_details()[0][\"index\"]\n",
        "\n",
        "interpreter.set_tensor(input_index, test_image)\n",
        "interpreter.invoke()\n",
        "predictions = interpreter.get_tensor(output_index)"
      ],
      "metadata": {
        "id": "FjnCfRY1dIst"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "  for test_image in test_images:\n",
        "    # Pre-processing: add batch dimension and convert to float32 to match with\n",
        "    # the model's input data format.\n",
        "    test_image = np.expand_dims(test_image, axis=0).astype(np.float32)\n",
        "    interpreter.set_tensor(input_index, test_image)\n",
        "\n",
        "    # Run inference.\n",
        "    interpreter.invoke()\n",
        "\n",
        "    # Post-processing: remove batch dimension and find the digit with highest\n",
        "    # probability.\n",
        "    output = interpreter.tensor(output_index)\n",
        "    digit = np.argmax(output()[0])\n",
        "    print(digit)\n",
        "    break;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xkgNtNzPddsv",
        "outputId": "7ca31275-9deb-4c2a-a2a9-5c89baa928ce"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_array=test_image[0]"
      ],
      "metadata": {
        "id": "xlAJFMx7duVt"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "_tmWRn4Vdy68"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "kXueRJ8qeHoq",
        "outputId": "5d9fb936-5e74-4527-e309-996413360b71"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7087d7f400>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I_nfFqhGdPdP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"provenance": [],
	"authorship_tag": "ABX9TyPVp9woTP8Lp7W1beiD3yHG",
	"include_colab_link": true
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3"
	},
	"language_info": {
	"name": "python"
	}
	},
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "view-in-github",
	"colab_type": "text"
	},
	"source": [
	"<a href=\"https://colab.research.google.com/gist/kiransair/0f0872c64fedd4a77fd6579842f314c1/tf_forum_23085.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/"
	},
	"id": "PsmJli3xJ_42",
	"outputId": "a3f5871b-90dd-401b-f6f5-40174bb844da"
	},
	"outputs": [
	{
	"output_type": "stream",
	"name": "stderr",
	"text": [
	"DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\n"
	]
	}
	],
	"source": [
	"import logging\n",
	"logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n",
	"\n",
	"import tensorflow as tf\n",
	"from tensorflow import keras\n",
	"import numpy as np\n",
	"import pathlib"
	]
	},
	{
	"cell_type": "code",
	"source": [
	"# Load MNIST dataset\n",
	"mnist = keras.datasets.mnist\n",
	"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
	"\n",
	"# Normalize the input image so that each pixel value is between 0 to 1.\n",
	"train_images = train_images / 255.0\n",
	"test_images = test_images / 255.0\n",
	"\n",
	"# Define the model architecture\n",
	"model = keras.Sequential([\n",
	" keras.layers.InputLayer(input_shape=(28, 28)),\n",
	" keras.layers.Reshape(target_shape=(28, 28, 1)),\n",
	" keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),\n",
	" keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
	" keras.layers.Flatten(),\n",
	" keras.layers.Dense(10)\n",
	"])\n",
	"\n",
	"# Train the digit classification model\n",
	"model.compile(optimizer='adam',\n",
	" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
	" metrics=['accuracy'])\n",
	"model.fit(\n",
	" train_images,\n",
	" train_labels,\n",
	" epochs=1,\n",
	" validation_data=(test_images, test_labels)\n",
	")"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/"
	},
	"id": "yO70mHplKFve",
	"outputId": "177d2a57-8209-4758-87ae-a0af69bb2f0b"
	},
	"execution_count": 2,
	"outputs": [
	{
	"output_type": "stream",
	"name": "stdout",
	"text": [
	"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
	"11490434/11490434 [==============================] - 0s 0us/step\n",
	"1875/1875 [==============================] - 36s 19ms/step - loss: 0.2908 - accuracy: 0.9194 - val_loss: 0.1409 - val_accuracy: 0.9575\n"
	]
	},
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"<keras.src.callbacks.History at 0x7f70b8489c30>"
	]
	},
	"metadata": {},
	"execution_count": 2
	}
	]
	},
	{
	"cell_type": "code",
	"source": [
	"converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
	"tflite_model = converter.convert()"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/"
	},
	"id": "-L_OK9jmc93w",
	"outputId": "ea6aa61d-a2d7-482c-c511-95845b5f029b"
	},
	"execution_count": 3,
	"outputs": [
	{
	"output_type": "stream",
	"name": "stderr",
	"text": [
	"INFO:tensorflow:Assets written to: /tmp/tmpqjec_r4i/assets\n"
	]
	}
	]
	},
	{
	"cell_type": "code",
	"source": [
	"tflite_models_dir = pathlib.Path(\"/tmp/mnist_tflite_models/\")\n",
	"tflite_models_dir.mkdir(exist_ok=True, parents=True)"
	],
	"metadata": {
	"id": "_jhJrOugdABe"
	},
	"execution_count": 4,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": [
	"tflite_model_file = tflite_models_dir/\"mnist_model.tflite\"\n",
	"tflite_model_file.write_bytes(tflite_model)"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/"
	},
	"id": "6umm3mw4dBmQ",
	"outputId": "90a240cc-0b3e-4150-e5ca-59a04ec6d1f2"
	},
	"execution_count": 5,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"84820"
	]
	},
	"metadata": {},
	"execution_count": 5
	}
	]
	},
	{
	"cell_type": "code",
	"source": [
	"converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
	"tflite_quant_model = converter.convert()\n",
	"tflite_model_quant_file = tflite_models_dir/\"mnist_model_quant.tflite\"\n",
	"tflite_model_quant_file.write_bytes(tflite_quant_model)"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/"
	},
	"id": "RyIS2ls5dDMd",
	"outputId": "458467c8-5eb6-4b4b-d845-c26bc6a702af"
	},
	"execution_count": 6,
	"outputs": [
	{
	"output_type": "stream",
	"name": "stderr",
	"text": [
	"INFO:tensorflow:Assets written to: /tmp/tmpj2jrid0z/assets\n"
	]
	},
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"24064"
	]
	},
	"metadata": {},
	"execution_count": 6
	}
	]
	},
	{
	"cell_type": "code",
	"source": [
	"interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))\n",
	"interpreter.allocate_tensors()"
	],
	"metadata": {
	"id": "b-WBeH0udFkk"
	},
	"execution_count": 7,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": [
	"interpreter_quant = tf.lite.Interpreter(model_path=str(tflite_model_quant_file))\n",
	"interpreter_quant.allocate_tensors()"
	],
	"metadata": {
	"id": "jpgmxJVSdHVc"
	},
	"execution_count": 8,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": [
	"test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)\n",
	"\n",
	"input_index = interpreter.get_input_details()[0][\"index\"]\n",
	"output_index = interpreter.get_output_details()[0][\"index\"]\n",
	"\n",
	"interpreter.set_tensor(input_index, test_image)\n",
	"interpreter.invoke()\n",
	"predictions = interpreter.get_tensor(output_index)"
	],
	"metadata": {
	"id": "FjnCfRY1dIst"
	},
	"execution_count": 9,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": [
	" for test_image in test_images:\n",
	" # Pre-processing: add batch dimension and convert to float32 to match with\n",
	" # the model's input data format.\n",
	" test_image = np.expand_dims(test_image, axis=0).astype(np.float32)\n",
	" interpreter.set_tensor(input_index, test_image)\n",
	"\n",
	" # Run inference.\n",
	" interpreter.invoke()\n",
	"\n",
	" # Post-processing: remove batch dimension and find the digit with highest\n",
	" # probability.\n",
	" output = interpreter.tensor(output_index)\n",
	" digit = np.argmax(output()[0])\n",
	" print(digit)\n",
	" break;"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/"
	},
	"id": "xkgNtNzPddsv",
	"outputId": "7ca31275-9deb-4c2a-a2a9-5c89baa928ce"
	},
	"execution_count": 16,
	"outputs": [
	{
	"output_type": "stream",
	"name": "stdout",
	"text": [
	"7\n"
	]
	}
	]
	},
	{
	"cell_type": "code",
	"source": [
	"image_array=test_image[0]"
	],
	"metadata": {
	"id": "xlAJFMx7duVt"
	},
	"execution_count": 17,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": [
	"import matplotlib.pyplot as plt"
	],
	"metadata": {
	"id": "_tmWRn4Vdy68"
	},
	"execution_count": 18,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": [
	"plt.imshow(image_array)"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 449
	},
	"id": "kXueRJ8qeHoq",
	"outputId": "5d9fb936-5e74-4527-e309-996413360b71"
	},
	"execution_count": 19,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"<matplotlib.image.AxesImage at 0x7f7087d7f400>"
	]
	},
	"metadata": {},
	"execution_count": 19
	},
	{
	"output_type": "display_data",
	"data": {
	"text/plain": [
	"<Figure size 640x480 with 1 Axes>"
	],
	"image/png": "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\n"
	},
	"metadata": {}
	}
	]
	},
	{
	"cell_type": "code",
	"source": [],
	"metadata": {
	"id": "I_nfFqhGdPdP"
	},
	"execution_count": null,
	"outputs": []
	}
	]
	}