date_accuracy_check4.ipynb

{
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  "metadata": {
    "colab": {
      "name": "date_accuracy_check4.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/iamrajee/Machine-Learning/blob/master/date_accuracy_check4.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "7JZNGCRUiY3l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(\"he\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f9qJBM1UamGH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4397e22d-82d8-4f9c-a0d5-9c8fc5a0f029"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datalab  drive\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HUua6OeJdT8T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "y_train_df = pd.read_csv(\"drive/ml_data/transfer_learning/train_date_data/datefinal.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "23PjO9YPdUvt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_all = y_train_df['0']\n",
        "for i,ele in enumerate(y_all):\n",
        "  y_all[i] = (ele-1900)//5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "woAkRCCNdm3k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelBinarizer\n",
        "encoder = LabelBinarizer()\n",
        "transfomed_label = encoder.fit_transform(y_all)\n",
        "Y = transfomed_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NANXC30KdoTZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "\n",
        "#\n",
        "import os\n",
        "import time\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.imagenet_utils import preprocess_input\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.cross_validation import train_test_split\n",
        "from keras import optimizers\n",
        "#\n",
        "\n",
        "\n",
        "from keras import applications\n",
        "from keras import optimizers\n",
        "from keras import backend as k\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D\n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard,EarlyStopping"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tucktBdYdqKM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "img_width, img_height = 256, 256\n",
        "\n",
        "# batch_size = 16\n",
        "# epochs = 50\n",
        "\n",
        "model = applications.VGG19(weights = \"imagenet\", include_top=False, input_shape = (img_width, img_height, 3))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tlgf__Dqd09P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_path = 'drive/ml_data/transfer_learning/image_data'\n",
        "\n",
        "img_data_list=[]\n",
        "\n",
        "\n",
        "from PIL import Image, ImageFile\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "\n",
        "img_list=os.listdir(data_path)\n",
        "\n",
        "img_list = sorted(img_list, key=lambda x: x)\n",
        "\n",
        "for img in img_list:\n",
        "\timg_path = data_path + '/'+ img \n",
        "\timg = image.load_img(img_path, target_size=(256, 256))\n",
        "\tx = image.img_to_array(img)\n",
        "\tx = np.expand_dims(x, axis=0)\n",
        "\tx = preprocess_input(x)\n",
        "# \tprint('Input image shape:', x.shape)\n",
        "\timg_data_list.append(x)\n",
        "#   if i%100==0:\n",
        "#     print(i)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n5maKvCvds_0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "e1f0be40-5491-4c8a-e37f-af43972856c1"
      },
      "cell_type": "code",
      "source": [
        "img_data = np.array(img_data_list)\n",
        "#img_data = img_data.astype('float32')\n",
        "print (img_data.shape)\n",
        "img_data=np.rollaxis(img_data,1,0)\n",
        "print (img_data.shape)\n",
        "img_data=img_data[0]\n",
        "print (img_data.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(910, 1, 256, 256, 3)\n",
            "(1, 910, 256, 256, 3)\n",
            "(910, 256, 256, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "O2MNgOxYdwwY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Shuffle the dataset\n",
        "x,y = shuffle(img_data,Y, random_state=2)\n",
        "# Split the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)\n",
        "\n",
        "num_classes = 16\n",
        "num_of_samples = img_data.shape[0]\n",
        "\n",
        "# print(np.shape(X_train))\n",
        "# print(np.shape(X_test))\n",
        "# print(np.shape(y_train))\n",
        "# print(np.shape(y_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HF_NTjEYd5Oa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#freeze the few(1st five) layers for undesirable change\n",
        "\n",
        "for layer in model.layers[:4]:\n",
        "  layer.trainable = False\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LFEoYGu6d7g-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#adding custom layers\n",
        "x = model.output\n",
        "x = Flatten()(x)\n",
        "x = Dense(1024, activation=\"relu\")(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = Dense(1024,activation=\"relu\")(x)\n",
        "predictions = Dense(num_classes,activation=\"softmax\", name='output_layer')(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8D3fq_S6d959",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "88c77078-86e7-44cf-e351-a27f81be7893"
      },
      "cell_type": "code",
      "source": [
        "#create final model\n",
        "model_final = Model(input=model.input, output = predictions)\n",
        "\n",
        "\n",
        "# model_final.summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"ou...)`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "6GmZODDTeZ3E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2907
        },
        "outputId": "b89c61b5-874b-40ab-b153-daf25b71602c"
      },
      "cell_type": "code",
      "source": [
        "model_final.compile(loss='categorical_crossentropy',optimizer= optimizers.RMSprop(lr=2e-4),metrics=['accuracy'])\n",
        "\n",
        "t=time.time()\n",
        "hist = model_final.fit(X_train, y_train, batch_size=8, epochs=30, verbose=1, validation_data=(X_test, y_test))\n",
        "print('Training time: %s' % (t - time.time()))\n",
        "(loss, accuracy) = model_final.evaluate(X_test, y_test, batch_size=10, verbose=1)\n",
        "\n",
        "print(\"[INFO] loss={:.4f}, accuracy: {:.4f}%\".format(loss,accuracy * 100))\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 728 samples, validate on 182 samples\n",
            "Epoch 1/30\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ResourceExhaustedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1322\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1323\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1306\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1307\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1408\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m           run_metadata)\n\u001b[0m\u001b[1;32m   1410\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor of shape [32768,1024] and type float\n\t [[Node: training/RMSprop/zeros_28 = Const[dtype=DT_FLOAT, value=Tensor<type: float shape: [32768,1024] values: [0 0 0]...>, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"]()]]",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-2c4d0d6fd544>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training time: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1703\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1234\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1236\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1237\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2478\u001b[0m             \u001b[0mfeed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m         \u001b[0mfetches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdates_op\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2480\u001b[0;31m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2481\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[1;32m   2482\u001b[0m                               **self.session_kwargs)\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mget_session\u001b[0;34m()\u001b[0m\n\u001b[1;32m    198\u001b[0m                     \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keras_initialized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muninitialized_vars\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m                     \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muninitialized_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m     \u001b[0;31m# hack for list_devices() function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;31m# list_devices() function is not available under tensorflow r1.3.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    898\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 900\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    901\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1135\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1136\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1137\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1314\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1316\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1317\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor of shape [32768,1024] and type float\n\t [[Node: training/RMSprop/zeros_28 = Const[dtype=DT_FLOAT, value=Tensor<type: float shape: [32768,1024] values: [0 0 0]...>, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"]()]]\n\nCaused by op 'training/RMSprop/zeros_28', defined at:\n  File \"/usr/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python3.6/dist-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"/usr/local/lib/python3.6/dist-packages/zmq/eventloop/ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2718, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2822, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-14-2c4d0d6fd544>\", line 4, in <module>\n    hist = model_final.fit(X_train, y_train, batch_size=8, epochs=30, verbose=1, validation_data=(X_test, y_test))\n  File \"/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\", line 1682, in fit\n    self._make_train_function()\n  File \"/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\", line 992, in _make_train_function\n    loss=self.total_loss)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\", line 91, in wrapper\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/optimizers.py\", line 245, in get_updates\n    accumulators = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n  File \"/usr/local/lib/python3.6/dist-packages/keras/optimizers.py\", line 245, in <listcomp>\n    accumulators = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n  File \"/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\", line 693, in zeros\n    v = tf.zeros(shape=shape, dtype=tf_dtype, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py\", line 1550, in zeros\n    output = fill(shape, constant(zero, dtype=dtype), name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py\", line 2793, in fill\n    \"Fill\", dims=dims, value=value, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\", line 3414, in create_op\n    op_def=op_def)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\", line 1740, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nResourceExhaustedError (see above for traceback): OOM when allocating tensor of shape [32768,1024] and type float\n\t [[Node: training/RMSprop/zeros_28 = Const[dtype=DT_FLOAT, value=Tensor<type: float shape: [32768,1024] values: [0 0 0]...>, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"]()]]\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "IWXdZKPpeeUi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# visualizing losses and accuracy\n",
        "train_loss=hist.history['loss']\n",
        "val_loss=hist.history['val_loss']\n",
        "train_acc=hist.history['acc']\n",
        "val_acc=hist.history['val_acc']\n",
        "xc=range(12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1-0Ykvhvegsx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plt.figure(1,figsize=(7,5))\n",
        "plt.plot(xc,train_loss)\n",
        "plt.plot(xc,val_loss)\n",
        "plt.xlabel('num of Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.title('train_loss vs val_loss')\n",
        "plt.grid(True)\n",
        "plt.legend(['train','val'])\n",
        "#print plt.style.available # use bmh, classic,ggplot for big pictures\n",
        "plt.style.use(['classic'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RkELkut6ekIi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}