Countries_Autocomplete_RNN.ipynb

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  "metadata": {
    "colab": {
      "name": "Countries_Autocomplete_RNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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": [
        "<a href=\"https://colab.research.google.com/github/babakansari/DeepLearning/blob/master/Countries_Autocomplete_RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIo8qT-r4Yto",
        "colab_type": "code",
        "outputId": "b559251b-0b88-4027-a03f-79671ae53fee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "import tensorflow as tf\n",
        "tf.set_random_seed(42)\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.layers import LSTM, Dropout\n",
        "from keras.layers import TimeDistributed\n",
        "from keras.layers.core import Dense, Activation, Dropout, RepeatVector\n",
        "from keras.optimizers import RMSprop\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "import sys\n",
        "import heapq\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 12, 5"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scWJO8-K4fFe",
        "colab_type": "code",
        "outputId": "3388f542-7559-44d6-cf41-f0da1e435369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "\n",
        "file_name = 'https://github.com/babakansari/DeepLearning/blob/master/Countries.xlsx?raw=true'\n",
        "\n",
        "original_df = pd.read_excel(file_name)\n",
        "original_df.head()\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Ext</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan (AF)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aland Islands (AX)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Albania (AL)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Algeria (DZ)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>American Samoa (AS)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Name  Ext\n",
              "0     Afghanistan (AF)  NaN\n",
              "1   Aland Islands (AX)  NaN\n",
              "2         Albania (AL)  NaN\n",
              "3         Algeria (DZ)  NaN\n",
              "4  American Samoa (AS)  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2ku8p6B4g6F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "85f8bf28-556a-4c0d-cfd1-78ee036be3fe"
      },
      "source": [
        "df = pd.DataFrame(columns=['idx', 'Name', 'Source', 'Target', 'Len'])\n",
        "\n",
        "idx = 0\n",
        "for index, row in original_df.iterrows():\n",
        "  name = row['Name']\n",
        "  nameLen = row['Name'].find('(')\n",
        "\n",
        "  for i, c in enumerate(name):\n",
        "    if i == nameLen-2:\n",
        "      break;\n",
        "    df.loc[idx] = [idx, name[0:nameLen], (name[0:i]+c).rjust(40, '_'), name[i+1:nameLen].rjust(40, '_'), i+1]\n",
        "    idx = idx + 1\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idx</th>\n",
              "      <th>Name</th>\n",
              "      <th>Source</th>\n",
              "      <th>Target</th>\n",
              "      <th>Len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>_______________________________________A</td>\n",
              "      <td>_____________________________fghanistan</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>______________________________________Af</td>\n",
              "      <td>______________________________ghanistan</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>_____________________________________Afg</td>\n",
              "      <td>_______________________________hanistan</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>____________________________________Afgh</td>\n",
              "      <td>________________________________anistan</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>___________________________________Afgha</td>\n",
              "      <td>_________________________________nistan</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  idx          Name  ...                                    Target Len\n",
              "0   0  Afghanistan   ...  _____________________________fghanistan    1\n",
              "1   1  Afghanistan   ...  ______________________________ghanistan    2\n",
              "2   2  Afghanistan   ...  _______________________________hanistan    3\n",
              "3   3  Afghanistan   ...  ________________________________anistan    4\n",
              "4   4  Afghanistan   ...  _________________________________nistan    5\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9V3GvKrytFu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df.sort_values('Source').head(20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xms1luw6GMMN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoded_df = pd.get_dummies(data=df, columns=['Source', 'Target'])\n",
        "\n",
        "# src_df = pd.get_dummies(data=df, columns=['Source'])\n",
        "\n",
        "# dest_df = pd.get_dummies(data=df, columns=['Target'])\n",
        "\n",
        "# src_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F707aufP-zUN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create list of Source and Target Columns\n",
        "src_cols = []\n",
        "trg_cols = []\n",
        "for column in list(encoded_df.columns.values):\n",
        "  if column.startswith(\"Source_\"):\n",
        "    src_cols.append(column)\n",
        "  elif column.startswith(\"Target_\"):\n",
        "      trg_cols.append(column)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwX-tKXf9rtN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "0ab6c64c-16c6-4825-f8cd-2cdcf0d45bd6"
      },
      "source": [
        "X = np.zeros((len(encoded_df), len(src_cols), len(trg_cols)), dtype=np.bool)\n",
        "\n",
        "for index, row in encoded_df.iterrows():\n",
        "  for src_idx, src in enumerate(src_cols):\n",
        "    if row[src] == 1:\n",
        "      for trg_idx, trg in enumerate(trg_cols):\n",
        "        if row[trg] == 1:\n",
        "          X[index, src_idx, trg_idx] = 1\n",
        "          #print(str(index) + \" = \" + str(src_idx) + \", \" + str(trg_idx))\n",
        "\n",
        "print(X)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  ...\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]]\n",
            "\n",
            " [[False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  ...\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]]\n",
            "\n",
            " [[False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  ...\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  ...\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]]\n",
            "\n",
            " [[False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  ...\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]]\n",
            "\n",
            " [[False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  ...\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hqmgs4Xm_fZP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5eb2d8d3-7320-45b1-bfde-8fd2472efdbf"
      },
      "source": [
        "src_df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2259, 1704)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLNySHGjY7jG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0da10b1-7557-442e-9c5d-570277841d83"
      },
      "source": [
        "name_enc.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2259, 258)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4s2UDPoc_NW2",
        "colab_type": "text"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zB6R5jQW-IPL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "24fdffbd-61a9-462c-b63f-ecaf1a1cdbf9"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(128, input_shape=(2259, 1700)))\n",
        "\n",
        "model.add(Dense(1700))\n",
        "model.add(Activation('softmax'))\n",
        "model.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_6 (LSTM)                (None, 128)               936448    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1700)              219300    \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 1700)              0         \n",
            "=================================================================\n",
            "Total params: 1,155,748\n",
            "Trainable params: 1,155,748\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vc0-kRaK_72c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JeoZ3Y55C6ig",
        "colab_type": "text"
      },
      "source": [
        "##Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D102BPnsC7hu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "ef443e0a-4603-4745-cdeb-df6c39ee7cd6"
      },
      "source": [
        "optimizer = RMSprop(lr=0.01)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "history = model.fit(source_enc, name_enc, validation_split=0.05, batch_size=128, epochs=20, shuffle=True).history"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-3c2a75cfbc8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRMSprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\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----> 3\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_enc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_enc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1087\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1088\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1089\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m   1090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m         \u001b[0;31m# Prepare validation data.\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/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m    755\u001b[0m             \u001b[0mfeed_input_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# Don't enforce the batch size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 757\u001b[0;31m             exception_prefix='input')\n\u001b[0m\u001b[1;32m    758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\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/keras/engine/training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    129\u001b[0m                         \u001b[0;34m': expected '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' to have '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m                         \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' dimensions, but got array '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m                         'with shape ' + str(data_shape))\n\u001b[0m\u001b[1;32m    132\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m                     \u001b[0mdata_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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;31mValueError\u001b[0m: Error when checking input: expected lstm_6_input to have 3 dimensions, but got array with shape (2259, 1700)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haj1RnnrYlXr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}