speech-to-text/8.dilated-cnn-rnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "dimension = 400\n",
    "vocab = \"EOS abcdefghijklmnopqrstuvwxyz'\"\n",
    "char2idx = {char: idx for idx, char in enumerate(vocab)}\n",
    "idx2char = {idx: char for idx, char in enumerate(vocab)}\n",
    "\n",
    "def text2idx(text):\n",
    "    text = re.sub(r'[^a-z ]', '', text.lower()).strip()\n",
    "    converted = [char2idx[char] for char in text]\n",
    "    return text, converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "GO = 1\n",
    "PAD = 0\n",
    "EOS = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "train_X, train_Y = [], []\n",
    "text_files = [f for f in os.listdir('spectrogram-train') if f.endswith('.npy')]\n",
    "for fpath in text_files:\n",
    "    try:\n",
    "        splitted = fpath.split('-')\n",
    "        if len(splitted) == 2:\n",
    "            splitted[1] = splitted[1].split('.')[1]\n",
    "            fpath = splitted[0] + '.' + splitted[1]\n",
    "        with open('data/' + fpath.replace('npy', 'txt')) as fopen:\n",
    "            text, converted = text2idx(fopen.read())\n",
    "        w = np.load('spectrogram-train/' + fpath)\n",
    "        if w.shape[1] != dimension:\n",
    "            continue\n",
    "        train_X.append(w)\n",
    "        train_Y.append(converted)\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X, test_Y = [], []\n",
    "text_files = [f for f in os.listdir('spectrogram-test') if f.endswith('.npy')]\n",
    "for fpath in text_files:\n",
    "    with open('data/' + fpath.replace('npy', 'txt')) as fopen:\n",
    "        text, converted = text2idx(fopen.read())\n",
    "    w = np.load('spectrogram-test/' + fpath)\n",
    "    if w.shape[1] != dimension:\n",
    "        continue\n",
    "    test_X.append(w)\n",
    "    test_Y.append(converted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def layer_norm(inputs, epsilon=1e-8):\n",
    "    mean, variance = tf.nn.moments(inputs, [-1], keep_dims=True)\n",
    "    normalized = (inputs - mean) / (tf.sqrt(variance + epsilon))\n",
    "    params_shape = inputs.get_shape()[-1:]\n",
    "    gamma = tf.get_variable('gamma', params_shape, tf.float32, tf.ones_initializer())\n",
    "    beta = tf.get_variable('beta', params_shape, tf.float32, tf.zeros_initializer())\n",
    "    return gamma * normalized + beta\n",
    "\n",
    "\n",
    "def cnn_block(x, dilation_rate, pad_sz, hidden_dim, kernel_size):\n",
    "    x = layer_norm(x)\n",
    "    pad = tf.zeros([tf.shape(x)[0], pad_sz, hidden_dim])\n",
    "    x =  tf.layers.conv1d(inputs = tf.concat([pad, x, pad], 1),\n",
    "                          filters = hidden_dim,\n",
    "                          kernel_size = kernel_size,\n",
    "                          dilation_rate = dilation_rate)\n",
    "    x = x[:, :-pad_sz, :]\n",
    "    x = tf.nn.relu(x)\n",
    "    return x\n",
    "\n",
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        num_layers,\n",
    "        size_layers,\n",
    "        learning_rate,\n",
    "        num_features,\n",
    "        dropout = 1.0,\n",
    "        kernel_size = 5\n",
    "    ):\n",
    "        self.X = tf.placeholder(tf.float32, [None, None, num_features])\n",
    "        self.label = tf.placeholder(tf.int32, [None, None])\n",
    "        self.Y_seq_len = tf.placeholder(tf.int32, [None])\n",
    "        self.Y = tf.sparse_placeholder(tf.int32)\n",
    "        seq_lens = tf.count_nonzero(\n",
    "            tf.reduce_sum(self.X, -1), 1, dtype = tf.int32\n",
    "        )\n",
    "        batch_size = tf.shape(self.X)[0]\n",
    "        \n",
    "        x = tf.layers.conv1d(self.X, size_layers, 1)\n",
    "        for i in range(num_layers):\n",
    "            dilation_rate = 2 ** i\n",
    "            pad_sz = (kernel_size - 1) * dilation_rate\n",
    "            with tf.variable_scope('block_%d'%i):\n",
    "                x += cnn_block(x, dilation_rate, pad_sz, size_layers, kernel_size)\n",
    "        print(x)\n",
    "        \n",
    "        def cells(size, reuse = False):\n",
    "            return tf.contrib.rnn.DropoutWrapper(\n",
    "                tf.nn.rnn_cell.GRUCell(\n",
    "                    size,\n",
    "                    reuse = reuse,\n",
    "                ),\n",
    "                state_keep_prob = dropout,\n",
    "                output_keep_prob = dropout,\n",
    "            )\n",
    "        \n",
    "        for n in range(num_layers):\n",
    "            (out_fw, out_bw), (\n",
    "                state_fw,\n",
    "                state_bw,\n",
    "            ) = tf.nn.bidirectional_dynamic_rnn(\n",
    "                cell_fw = cells(size_layers),\n",
    "                cell_bw = cells(size_layers),\n",
    "                inputs = x,\n",
    "                sequence_length = seq_lens,\n",
    "                dtype = tf.float32,\n",
    "                scope = 'bidirectional_rnn_%d' % (n),\n",
    "            )\n",
    "            x = tf.concat((out_fw, out_bw), 2)\n",
    "        \n",
    "        logits = tf.layers.dense(x, len(vocab))\n",
    "        time_major = tf.transpose(logits, [1, 0, 2])\n",
    "        decoded, log_prob = tf.nn.ctc_beam_search_decoder(time_major, seq_lens)\n",
    "        decoded = tf.to_int32(decoded[0])\n",
    "        self.preds = tf.sparse.to_dense(decoded)\n",
    "        self.cost = tf.reduce_mean(\n",
    "            tf.nn.ctc_loss(\n",
    "                self.Y,\n",
    "                time_major,\n",
    "                seq_lens\n",
    "            )\n",
    "        )\n",
    "        self.optimizer = tf.train.AdamOptimizer(\n",
    "            learning_rate = learning_rate\n",
    "        ).minimize(self.cost)\n",
    "        \n",
    "        preds = self.preds[:, :tf.reduce_max(self.Y_seq_len)]\n",
    "        masks = tf.sequence_mask(self.Y_seq_len, tf.reduce_max(self.Y_seq_len), dtype=tf.float32)\n",
    "        preds = tf.pad(preds, [[0, 0], [0, tf.reduce_max(self.Y_seq_len)]])\n",
    "        y_t = tf.cast(preds, tf.int32)\n",
    "        self.prediction = tf.boolean_mask(y_t, masks)\n",
    "        mask_label = tf.boolean_mask(self.label, masks)\n",
    "        correct_pred = tf.equal(self.prediction, mask_label)\n",
    "        correct_index = tf.cast(correct_pred, tf.float32)\n",
    "        self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0830 22:08:40.840900 140199276345152 deprecation.py:506] From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507: calling count_nonzero (from tensorflow.python.ops.math_ops) with axis is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "reduction_indices is deprecated, use axis instead\n",
      "W0830 22:08:40.851948 140199276345152 deprecation.py:323] From <ipython-input-6-e1775f97eaaa>:40: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv1D` instead.\n",
      "W0830 22:08:40.857681 140199276345152 deprecation.py:506] From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"block_1/add_2:0\", shape=(?, ?, 512), dtype=float32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0830 22:08:41.879571 140199276345152 lazy_loader.py:50] \n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "W0830 22:08:41.880623 140199276345152 deprecation.py:323] From <ipython-input-6-e1775f97eaaa>:52: GRUCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.GRUCell, and will be replaced by that in Tensorflow 2.0.\n",
      "W0830 22:08:41.887044 140199276345152 deprecation.py:323] From <ipython-input-6-e1775f97eaaa>:68: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API\n",
      "W0830 22:08:41.887933 140199276345152 deprecation.py:323] From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "W0830 22:08:41.969996 140199276345152 deprecation.py:506] From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/rnn_cell_impl.py:564: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W0830 22:08:41.983373 140199276345152 deprecation.py:506] From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/rnn_cell_impl.py:574: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W0830 22:08:42.168517 140199276345152 deprecation.py:323] From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py:244: 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",
      "W0830 22:08:42.700160 140199276345152 deprecation.py:323] From <ipython-input-6-e1775f97eaaa>:72: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "W0830 22:08:42.981216 140199276345152 deprecation.py:323] From <ipython-input-6-e1775f97eaaa>:75: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "size_layers = 512\n",
    "learning_rate = 1e-4\n",
    "num_layers = 2\n",
    "batch_size = 64\n",
    "epoch = 20\n",
    "\n",
    "model = Model(num_layers, size_layers, learning_rate, dimension)\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = tf.keras.preprocessing.sequence.pad_sequences(\n",
    "    train_X, dtype = 'float32', padding = 'post'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = tf.keras.preprocessing.sequence.pad_sequences(\n",
    "    test_X, dtype = 'float32', padding = 'post'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_sentence_batch(sentence_batch, pad_int):\n",
    "    padded_seqs = []\n",
    "    seq_lens = []\n",
    "    max_sentence_len = max([len(sentence) for sentence in sentence_batch])\n",
    "    for sentence in sentence_batch:\n",
    "        padded_seqs.append(sentence + [pad_int] * (max_sentence_len - len(sentence)))\n",
    "        seq_lens.append(len(sentence))\n",
    "    return padded_seqs, seq_lens\n",
    "\n",
    "def sparse_tuple_from(sequences, dtype=np.int32):\n",
    "    indices = []\n",
    "    values = []\n",
    "\n",
    "    for n, seq in enumerate(sequences):\n",
    "        indices.extend(zip([n] * len(seq), range(len(seq))))\n",
    "        values.extend(seq)\n",
    "\n",
    "    indices = np.asarray(indices, dtype=np.int64)\n",
    "    values = np.asarray(values, dtype=dtype)\n",
    "    shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)\n",
    "\n",
    "    return indices, values, shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:39<00:00,  1.58it/s, accuracy=0.101, cost=8.6]  \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:07<00:00,  1.32it/s, accuracy=0.0556, cost=10.4]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, training avg cost 17.430222, training avg accuracy 0.055180\n",
      "epoch 1, testing avg cost 10.321519, testing avg accuracy 0.043862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:48<00:00,  1.66it/s, accuracy=0.388, cost=4.22] \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.37it/s, accuracy=0.058, cost=8.77] \n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2, training avg cost 6.993067, training avg accuracy 0.104283\n",
      "epoch 2, testing avg cost 8.701987, testing avg accuracy 0.163437\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:44<00:00,  1.60it/s, accuracy=0.403, cost=1.42] \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.39it/s, accuracy=0.0507, cost=8.72]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3, training avg cost 3.542868, training avg accuracy 0.214028\n",
      "epoch 3, testing avg cost 8.529030, testing avg accuracy 0.199252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:45<00:00,  1.57it/s, accuracy=0.403, cost=0.435] \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.39it/s, accuracy=0.0531, cost=9.42]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4, training avg cost 1.445465, training avg accuracy 0.328664\n",
      "epoch 4, testing avg cost 9.199284, testing avg accuracy 0.240092\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:46<00:00,  1.57it/s, accuracy=0.403, cost=0.313]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.39it/s, accuracy=0.0507, cost=10.2]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 5, training avg cost 0.656450, training avg accuracy 0.382602\n",
      "epoch 5, testing avg cost 9.518508, testing avg accuracy 0.315900\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:46<00:00,  1.61it/s, accuracy=0.403, cost=0.194]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.41it/s, accuracy=0.0519, cost=11.1]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 6, training avg cost 0.352593, training avg accuracy 0.387840\n",
      "epoch 6, testing avg cost 10.177751, testing avg accuracy 0.316750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:48<00:00,  1.53it/s, accuracy=0.403, cost=0.0492]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.41it/s, accuracy=0.0507, cost=11.5]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 7, training avg cost 0.184272, training avg accuracy 0.394607\n",
      "epoch 7, testing avg cost 10.519309, testing avg accuracy 0.277311\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:48<00:00,  1.59it/s, accuracy=0.403, cost=0.0322]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.35it/s, accuracy=0.0495, cost=11.7]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 8, training avg cost 0.109800, training avg accuracy 0.394629\n",
      "epoch 8, testing avg cost 10.735456, testing avg accuracy 0.314960\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:48<00:00,  1.57it/s, accuracy=0.403, cost=0.0252]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.38it/s, accuracy=0.0507, cost=12.1]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 9, training avg cost 0.075501, training avg accuracy 0.394629\n",
      "epoch 9, testing avg cost 10.974558, testing avg accuracy 0.277309\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:49<00:00,  1.57it/s, accuracy=0.403, cost=0.017] \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.40it/s, accuracy=0.0507, cost=12.1]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 10, training avg cost 0.050798, training avg accuracy 0.394633\n",
      "epoch 10, testing avg cost 11.132065, testing avg accuracy 0.277105\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:51<00:00,  1.53it/s, accuracy=0.403, cost=0.0132]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.37it/s, accuracy=0.0495, cost=12.1]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 11, training avg cost 0.037048, training avg accuracy 0.394633\n",
      "epoch 11, testing avg cost 11.185449, testing avg accuracy 0.277377\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:52<00:00,  1.55it/s, accuracy=0.403, cost=0.0353] \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.39it/s, accuracy=0.0568, cost=12.5]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 12, training avg cost 0.044216, training avg accuracy 0.389528\n",
      "epoch 12, testing avg cost 12.464685, testing avg accuracy 0.351144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:51<00:00,  1.56it/s, accuracy=0.403, cost=0.0199]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.38it/s, accuracy=0.0507, cost=12.5]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 13, training avg cost 0.220696, training avg accuracy 0.386060\n",
      "epoch 13, testing avg cost 11.664711, testing avg accuracy 0.315911\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:53<00:00,  1.56it/s, accuracy=0.403, cost=0.0115] \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.38it/s, accuracy=0.386, cost=13]   \n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 14, training avg cost 0.040876, training avg accuracy 0.392870\n",
      "epoch 14, testing avg cost 12.251837, testing avg accuracy 0.351381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:54<00:00,  1.54it/s, accuracy=0.403, cost=0.00871]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.33it/s, accuracy=0.0519, cost=13]  \n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 15, training avg cost 0.018406, training avg accuracy 0.394629\n",
      "epoch 15, testing avg cost 11.976722, testing avg accuracy 0.278261\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:54<00:00,  1.55it/s, accuracy=0.403, cost=0.00478]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.40it/s, accuracy=0.0531, cost=13.2]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 16, training avg cost 0.010608, training avg accuracy 0.394633\n",
      "epoch 16, testing avg cost 12.082260, testing avg accuracy 0.316379\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:51<00:00,  1.59it/s, accuracy=0.403, cost=0.00379]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.40it/s, accuracy=0.0543, cost=13.2]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 17, training avg cost 0.008056, training avg accuracy 0.394633\n",
      "epoch 17, testing avg cost 12.205802, testing avg accuracy 0.316209\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:51<00:00,  1.55it/s, accuracy=0.403, cost=0.00316]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.41it/s, accuracy=0.0531, cost=13.4]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 18, training avg cost 0.006552, training avg accuracy 0.394633\n",
      "epoch 18, testing avg cost 12.361527, testing avg accuracy 0.315976\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:52<00:00,  1.56it/s, accuracy=0.403, cost=0.0027] \n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.37it/s, accuracy=0.0531, cost=13.6]\n",
      "minibatch loop:   0%|          | 0/206 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 19, training avg cost 0.005566, training avg accuracy 0.394633\n",
      "epoch 19, testing avg cost 12.499822, testing avg accuracy 0.316181\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 206/206 [02:51<00:00,  1.56it/s, accuracy=0.403, cost=0.00232]\n",
      "testing minibatch loop: 100%|██████████| 9/9 [00:06<00:00,  1.40it/s, accuracy=0.0531, cost=13.7]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20, training avg cost 0.004802, training avg accuracy 0.394633\n",
      "epoch 20, testing avg cost 12.632605, testing avg accuracy 0.316081\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "for e in range(epoch):\n",
    "    pbar = tqdm(\n",
    "        range(0, len(train_X), batch_size), desc = 'minibatch loop')\n",
    "    train_cost, train_accuracy, test_cost, test_accuracy = [], [], [], []\n",
    "    for i in pbar:\n",
    "        batch_x = train_X[i : min(i + batch_size, len(train_X))]\n",
    "        y = train_Y[i : min(i + batch_size, len(train_X))]\n",
    "        batch_y = sparse_tuple_from(y)\n",
    "        batch_label, batch_len = pad_sentence_batch(y, 0)\n",
    "        _, cost, accuracy = sess.run(\n",
    "            [model.optimizer, model.cost, model.accuracy],\n",
    "            feed_dict = {model.X: batch_x, model.Y: batch_y, \n",
    "                         model.label: batch_label, model.Y_seq_len: batch_len},\n",
    "        )\n",
    "        train_cost.append(cost)\n",
    "        train_accuracy.append(accuracy)\n",
    "        pbar.set_postfix(cost = cost, accuracy = accuracy)\n",
    "    \n",
    "    pbar = tqdm(\n",
    "        range(0, len(test_X), batch_size), desc = 'testing minibatch loop')\n",
    "    for i in pbar:\n",
    "        batch_x = test_X[i : min(i + batch_size, len(test_X))]\n",
    "        y = test_Y[i : min(i + batch_size, len(test_X))]\n",
    "        batch_y = sparse_tuple_from(y)\n",
    "        batch_label, batch_len = pad_sentence_batch(y, 0)\n",
    "        cost, accuracy = sess.run(\n",
    "            [model.cost, model.accuracy],\n",
    "            feed_dict = {model.X: batch_x, model.Y: batch_y, \n",
    "                         model.label: batch_label, model.Y_seq_len: batch_len},\n",
    "        )\n",
    "        \n",
    "        test_cost.append(cost)\n",
    "        test_accuracy.append(accuracy)\n",
    "        \n",
    "        pbar.set_postfix(cost = cost, accuracy = accuracy)\n",
    "    print('epoch %d, training avg cost %f, training avg accuracy %f'%(e + 1, np.mean(train_cost), \n",
    "                                                                      np.mean(train_accuracy)))\n",
    "    \n",
    "    print('epoch %d, testing avg cost %f, testing avg accuracy %f'%(e + 1, np.mean(test_cost), \n",
    "                                                                    np.mean(test_accuracy)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "real: say the word dip\n",
      "predicted: say the word fip\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "random_index = random.randint(0, len(test_X) - 1)\n",
    "batch_x = test_X[random_index : random_index + 1]\n",
    "print(\n",
    "    'real:',\n",
    "    ''.join(\n",
    "        [idx2char[no] for no in test_Y[random_index : random_index + 1][0]]\n",
    "    ),\n",
    ")\n",
    "batch_y = sparse_tuple_from(test_Y[random_index : random_index + 1])\n",
    "pred = sess.run(model.preds, feed_dict = {model.X: batch_x})[0]\n",
    "print('predicted:', ''.join([idx2char[no] for no in pred]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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