Taylor_series.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Taylor_series.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM1xSchXID8v5VhxVNfEM5M",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jvishnuvardhan/Stackoverflow_Questions/blob/master/Taylor_series.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-wPcOY7ZQsM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "a7c39988-9ce3-457f-c3e7-50dc13f5fd86"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AhwXC4TnDrH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np \n",
        "from tensorflow import keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udgrUo8Anqq9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esSh_dvoZS53",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "4d7d625b-1236-46b9-856b-aa13a3e1c82a"
      },
      "source": [
        "from tensorflow.keras.optimizers import Adamax\n",
        "from keras.datasets import cifar10\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "(train_imgs, train_label), (test_imgs, test_label)= cifar10.load_data()\n",
        "output_class = np.unique(train_label)\n",
        "n_class = len(output_class)\n",
        "train_label_one_hot= to_categorical(train_label,n_class)\n",
        "test_label_one_hot= to_categorical(test_label,n_class)\n",
        "\n",
        "def resNet_2(input_img, exp_order=2):\n",
        "    input_shape = input_img.shape[1:]\n",
        "    print(input_shape)\n",
        "    input_dim = input_shape[0]*input_shape[1]*input_shape[2]\n",
        "    x = Input((input_shape[0], input_shape[1], input_shape[2]))\n",
        "    print(x)\n",
        "    h = tf.reshape(x, (-1, input_dim))\n",
        "\n",
        "    def get_exp(x, exp_order):\n",
        "        x_ = x[..., None] \n",
        "        x_ = tf.tile(x_, multiples=[1, 1, exp_order + 1]) \n",
        "        pows = tf.range(0, exp_order + 1, dtype=tf.float32)\n",
        "        x_p = tf.pow(x_, pows)\n",
        "        return x_p\n",
        "\n",
        "    h = get_exp(h, exp_order=0)\n",
        "    h = tf.cumsum(h, axis=-1)\n",
        "    print('Hi')\n",
        "    print(input_shape[0], input_shape[1], input_shape[2])\n",
        "    tf.shape(h)\n",
        "    \n",
        "    h = tf.reshape(h, (-1, input_shape[0], input_shape[1], input_shape[2]))\n",
        "    h = Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu', input_shape=input_shape)(h)\n",
        "    h = Conv2D(filters=32, kernel_size=(3,3), activation='relu')(h)\n",
        "    h = MaxPooling2D(pool_size=(2,2))(h)\n",
        "    h = Dropout(0.25)(h)\n",
        "    h = Flatten()(h)\n",
        "    h = Dense(512, activation='relu')(h)\n",
        "    h = Dense(10, activation='softmax')(h)\n",
        "    model = Model(inputs=x, outputs=h)\n",
        "    return model\n",
        "\n",
        "my_model = resNet_2(train_imgs)\n",
        "my_model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=\"accuracy\")\n",
        "model_history = my_model.fit(train_imgs, train_label_one_hot, batch_size=64, epochs=20, validation_data=(test_imgs, test_label_one_hot))\n",
        "my_model.evaluate(train_imgs, train_label_one_hot)\n",
        "my_model.evaluate(test_imgs, test_label_one_hot)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 32, 3)\n",
            "Tensor(\"input_19:0\", shape=(None, 32, 32, 3), dtype=float32)\n",
            "Hi\n",
            "32 32 3\n",
            "Epoch 1/20\n",
            "782/782 [==============================] - 155s 198ms/step - loss: 2.3124 - accuracy: 0.0994 - val_loss: 2.3026 - val_accuracy: 0.1000\n",
            "Epoch 2/20\n",
            "782/782 [==============================] - 156s 199ms/step - loss: 2.3027 - accuracy: 0.0968 - val_loss: 2.3026 - val_accuracy: 0.1000\n",
            "Epoch 3/20\n",
            "140/782 [====>.........................] - ETA: 2:00 - loss: 2.3026 - accuracy: 0.1008"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBZxUJnFrg8U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8cbf4838-b62f-4b86-90c4-915b4b76ddd8"
      },
      "source": [
        "32*32*3/64"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FL1A9GaqADD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "62170459-3cb1-43cc-b804-d9b5cc1aaef8"
      },
      "source": [
        "my_model.summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_11 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_Reshape_19 (Tens [(None, 3072)]            0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_strided_slice_9  [(None, 3072, 1)]         0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_Tile_9 (TensorFl [(None, 3072, 3)]         0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_Pow_9 (TensorFlo [(None, 3072, 3)]         0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_Cumsum_9 (Tensor [(None, 3072, 3)]         0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_Reshape_20 (Tens [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 30, 30, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 7200)              0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 512)               3686912   \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 3,702,186\n",
            "Trainable params: 3,702,186\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BItfWKAeZXME",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "}d = tf.add([a,b])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dlMxA5Xevv-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2e275686-88b7-4738-a5f2-dec484d39a7d"
      },
      "source": [
        "print(d)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor([[3. 3. 3.]], shape=(1, 3), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbYzeE33ezgA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}