diff --git a/keras_tf_sample.ipynb b/keras_tf_sample.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "keras-tf-sample.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "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/hrkt/Plugins/blob/master/keras_tf_sample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zy5ichg1VA97",
+        "colab_type": "text"
+      },
+      "source": [
+        "以下のサイトを参考にしました。\n",
+        "- 「Keras / Tensorflowで始めるディープラーニング入門」 https://qiita.com/yampy/items/706d44417c433e68db0d"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7jRl9looUSZ9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "b0c3cd80-32ac-413d-d585-b103ddadcda7"
+      },
+      "source": [
+        "import keras\n",
+        "from keras.datasets import mnist\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Dense, Dropout, Flatten\n",
+        "from keras.layers import Conv2D, MaxPooling2D\n",
+        "from keras import backend as K"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Using TensorFlow backend.\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7tTXTQn0UyGi",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "batch_size = 128\n",
+        "num_classes = 10\n",
+        "epochs = 3\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "trCXc0XGU1xt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "img_rows, img_cols = 28, 28\n",
+        "\n",
+        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9z32qSQqU9Px",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#Kerasのバックエンドで動くTensorFlowとTheanoでは入力チャンネルの順番が違うので場合分けして書いています\n",
+        "if K.image_data_format() == 'channels_first':\n",
+        "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
+        "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
+        "    input_shape = (1, img_rows, img_cols)\n",
+        "else:\n",
+        "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
+        "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
+        "    input_shape = (img_rows, img_cols, 1)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "du1XCGbBVECT",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        },
+        "outputId": "aadeacb2-91e6-4e05-d707-b9ece60eaef0"
+      },
+      "source": [
+        "x_train = x_train.astype('float32')\n",
+        "x_test = x_test.astype('float32')\n",
+        "x_train /= 255\n",
+        "x_test /= 255\n",
+        "print('x_train shape:', x_train.shape)\n",
+        "print(x_train.shape[0], 'train samples')\n",
+        "print(x_test.shape[0], 'test samples')"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "x_train shape: (60000, 28, 28, 1)\n",
+            "60000 train samples\n",
+            "10000 test samples\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AqnRhJyoVrjO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "y_train = y_train.astype('int32')\n",
+        "y_test = y_test.astype('int32')\n",
+        "y_train = keras.utils.np_utils.to_categorical(y_train, num_classes)\n",
+        "y_test =  keras.utils.np_utils.to_categorical(y_test, num_classes)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CSklWGNjVvOR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 315
+        },
+        "outputId": "ce8a8390-6d52-4e43-b0aa-6ab33ce0470c"
+      },
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Conv2D(32, kernel_size=(3, 3),\n",
+        "                 activation='relu',\n",
+        "                 input_shape=input_shape))\n",
+        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+        "model.add(Dropout(0.25))\n",
+        "model.add(Flatten())\n",
+        "model.add(Dense(128, activation='relu'))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(num_classes, activation='softmax'))"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING: Logging before flag parsing goes to stderr.\n",
+            "W0815 22:39:48.780859 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+            "\n",
+            "W0815 22:39:48.803259 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "\n",
+            "W0815 22:39:48.809604 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+            "\n",
+            "W0815 22:39:48.843924 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+            "\n",
+            "W0815 22:39:48.847448 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
+            "\n",
+            "W0815 22:39:48.860003 139626640222080 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "j_d10YewWISw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 105
+        },
+        "outputId": "a3d42703-5820-47ca-e5c1-737362237107"
+      },
+      "source": [
+        "model.compile(loss=keras.losses.categorical_crossentropy,\n",
+        "              optimizer=keras.optimizers.Adadelta(),\n",
+        "              metrics=['accuracy'])"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "W0815 22:39:48.964365 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+            "\n",
+            "W0815 22:39:48.975083 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+            "\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "D_Yuht6EWMpx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "b422c382-6e64-4e24-d720-5528a99dd734"
+      },
+      "source": [
+        "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,\n",
+        "          verbose=1, validation_data=(x_test, y_test))"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "W0815 22:39:49.110717 139626640222080 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Train on 60000 samples, validate on 10000 samples\n",
+            "Epoch 1/3\n",
+            "60000/60000 [==============================] - 158s 3ms/step - loss: 0.2604 - acc: 0.9192 - val_loss: 0.0632 - val_acc: 0.9808\n",
+            "Epoch 2/3\n",
+            "60000/60000 [==============================] - 157s 3ms/step - loss: 0.0910 - acc: 0.9732 - val_loss: 0.0436 - val_acc: 0.9855\n",
+            "Epoch 3/3\n",
+            "60000/60000 [==============================] - 157s 3ms/step - loss: 0.0650 - acc: 0.9805 - val_loss: 0.0370 - val_acc: 0.9870\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7efd2228df98>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file