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diff --git a/harsh.pv07@gmail.com/AItech/Aitech.ipynb b/harsh.pv07@gmail.com/AItech/Aitech.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Aitech.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uHUWnHs5hhBR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import numpy\n",
+        "from keras.datasets import mnist\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Dense\n",
+        "from keras.layers import Dropout\n",
+        "from keras.layers import Flatten\n",
+        "from keras.layers.convolutional import Conv2D\n",
+        "from keras.layers.convolutional import MaxPooling2D\n",
+        "from keras.utils import np_utils\n",
+        "from keras import backend as K\n",
+        "K.set_image_dim_ordering('th')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gbXkMBcAhlVJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "seed = 7\n",
+        "numpy.random.seed(seed)\n",
+        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+        "X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')\n",
+        "X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')\n",
+        "X_train = X_train / 255\n",
+        "X_test = X_test / 255\n",
+        "y_train = np_utils.to_categorical(y_train)\n",
+        "y_test = np_utils.to_categorical(y_test)\n",
+        "num_classes = y_test.shape[1]\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nab9ivn9iexH",
+        "colab_type": "code",
+        "outputId": "fb4cf6b6-7087-4d08-a1b0-ed8cb10e0c5c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        }
+      },
+      "source": [
+        "def baseline_model():\n",
+        "\tmodel = Sequential()\n",
+        "\tmodel.add(Conv2D(32, (5, 5), input_shape=(1, 28, 28), activation='relu'))\n",
+        "\tmodel.add(MaxPooling2D(pool_size=(2, 2)))\n",
+        "\tmodel.add(Dropout(0.2))\n",
+        "\tmodel.add(Flatten())\n",
+        "\tmodel.add(Dense(128, activation='relu'))\n",
+        "\tmodel.add(Dense(num_classes, activation='softmax'))\n",
+        "\tmodel.compile(loss='categorical_crossentropy', optimizer='AdaGrad', metrics=['accuracy'])\n",
+        "\treturn model\n",
+        "model = baseline_model()\n",
+        "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)\n",
+        "scores = model.evaluate(X_test, y_test, verbose=0)\n",
+        "print(\"CNN Error: %.2f%%\" % (100-scores[1]*100))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Train on 60000 samples, validate on 10000 samples\n",
+            "Epoch 1/10\n",
+            " - 4s - loss: 0.1801 - acc: 0.9463 - val_loss: 0.0683 - val_acc: 0.9786\n",
+            "Epoch 2/10\n",
+            " - 3s - loss: 0.0675 - acc: 0.9803 - val_loss: 0.0476 - val_acc: 0.9846\n",
+            "Epoch 3/10\n",
+            " - 3s - loss: 0.0523 - acc: 0.9844 - val_loss: 0.0434 - val_acc: 0.9860\n",
+            "Epoch 4/10\n",
+            " - 3s - loss: 0.0432 - acc: 0.9872 - val_loss: 0.0407 - val_acc: 0.9858\n",
+            "Epoch 5/10\n",
+            " - 3s - loss: 0.0381 - acc: 0.9883 - val_loss: 0.0372 - val_acc: 0.9872\n",
+            "Epoch 6/10\n",
+            " - 3s - loss: 0.0338 - acc: 0.9904 - val_loss: 0.0373 - val_acc: 0.9879\n",
+            "Epoch 7/10\n",
+            " - 3s - loss: 0.0310 - acc: 0.9908 - val_loss: 0.0368 - val_acc: 0.9873\n",
+            "Epoch 8/10\n",
+            " - 3s - loss: 0.0285 - acc: 0.9912 - val_loss: 0.0326 - val_acc: 0.9881\n",
+            "Epoch 9/10\n",
+            " - 3s - loss: 0.0259 - acc: 0.9922 - val_loss: 0.0315 - val_acc: 0.9887\n",
+            "Epoch 10/10\n",
+            " - 3s - loss: 0.0244 - acc: 0.9928 - val_loss: 0.0314 - val_acc: 0.9897\n",
+            "CNN Error: 1.03%\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zkrI00qUj9sp",
+        "colab_type": "code",
+        "outputId": "c7824b9c-6c43-420f-a263-5afb71a98f19",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "from matplotlib import pyplot as plt\n",
+        "plt.plot(history.history['acc'])\n",
+        "plt.plot(history.history['val_acc'])\n",
+        "plt.title('model accuracy')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.legend(['train', 'val'], loc='upper left')\n",
+        "plt.show()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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cOiVvG6smEuXspaUaLVZEpq1kd3AnX9lyqL4BPvz7pATFntZu6pu7qdYkRSIy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\nQi9yAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hT7GTV44Fzdx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file