notebooks/unconditional_lyrics_training.ipynb

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   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import random\n",
    "import string\n",
    "import numpy as np\n",
    "\n",
    "import sys, os\n",
    "\n",
    "import torch.utils.data as data\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
    "\n",
    "all_characters = string.printable\n",
    "number_of_characters = len(all_characters)\n",
    "\n",
    "\n",
    "def character_to_label(character):\n",
    "    \"\"\"Returns a one-hot-encoded tensor given a character.\n",
    "    \n",
    "    Uses string.printable as a dictionary.\n",
    "        \n",
    "    Parameters\n",
    "    ----------\n",
    "    character : str\n",
    "        A character\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    one_hot_tensor : Tensor of shape (1, number_of_characters)\n",
    "        One-hot-encoded tensor\n",
    "    \"\"\"\n",
    "    \n",
    "    character_label = all_characters.find(character)\n",
    "        \n",
    "    return character_label\n",
    "\n",
    "def string_to_labels(character_string):\n",
    "    \n",
    "    return map(lambda character: character_to_label(character), character_string)\n",
    "\n",
    "def pad_sequence(seq, max_length, pad_label=100):\n",
    "    \n",
    "    seq += [pad_label for i in range(max_length - len(seq))]\n",
    "    \n",
    "    return seq\n",
    "\n",
    "\n",
    "class LyricsGenerationDataset(data.Dataset):\n",
    "    \n",
    "    def __init__(self, csv_file_path, minimum_song_count=None, artists=None):\n",
    "        \n",
    "        \n",
    "        self.lyrics_dataframe = pd.read_csv(csv_file_path)\n",
    "        \n",
    "        if artists:\n",
    "            \n",
    "            self.lyrics_dataframe = self.lyrics_dataframe[self.lyrics_dataframe.artist.isin(artists)]\n",
    "            self.lyrics_dataframe = self.lyrics_dataframe.reset_index()\n",
    "        \n",
    "        if minimum_song_count:\n",
    "        \n",
    "            # Getting artists that have 70+ songs\n",
    "            self.lyrics_dataframe = self.lyrics_dataframe.groupby('artist').filter(lambda x: len(x) > minimum_song_count)\n",
    "            # Reindex .loc after we fetched random songs\n",
    "            self.lyrics_dataframe = self.lyrics_dataframe.reset_index()\n",
    "        \n",
    "        # Get the length of the biggest lyric text\n",
    "        # We will need that for padding\n",
    "        self.max_text_len = self.lyrics_dataframe.text.str.len().max()\n",
    "        \n",
    "        whole_dataset_len = len(self.lyrics_dataframe)\n",
    "        \n",
    "        self.indexes = range(whole_dataset_len)\n",
    "        \n",
    "        self.artists_list = list(self.lyrics_dataframe.artist.unique())\n",
    "        \n",
    "        self.number_of_artists = len(self.artists_list)\n",
    "    \n",
    "    \n",
    "    def __len__(self):\n",
    "        \n",
    "        return len(self.indexes)\n",
    "    \n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        \n",
    "        index = self.indexes[index]\n",
    "        \n",
    "        sequence_raw_string = self.lyrics_dataframe.loc[index].text\n",
    "        \n",
    "        sequence_string_labels = string_to_labels(sequence_raw_string)\n",
    "        \n",
    "        sequence_length = len(sequence_string_labels) - 1\n",
    "        \n",
    "        # Shifted by one char\n",
    "        input_string_labels = sequence_string_labels[:-1]\n",
    "        output_string_labels = sequence_string_labels[1:]\n",
    "                \n",
    "        # pad sequence so that all of them have the same lenght\n",
    "        # Otherwise the batching won't work\n",
    "        input_string_labels_padded = pad_sequence(input_string_labels, max_length=self.max_text_len)\n",
    "        \n",
    "        output_string_labels_padded = pad_sequence(output_string_labels, max_length=self.max_text_len, pad_label=-100)\n",
    "        \n",
    "        return (torch.LongTensor(input_string_labels_padded),\n",
    "                torch.LongTensor(output_string_labels_padded),\n",
    "                torch.LongTensor([sequence_length]) )\n",
    "\n",
    "    \n",
    "def post_process_sequence_batch(batch_tuple):\n",
    "    \n",
    "    input_sequences, output_sequences, lengths = batch_tuple\n",
    "    \n",
    "    splitted_input_sequence_batch = input_sequences.split(split_size=1)\n",
    "    splitted_output_sequence_batch = output_sequences.split(split_size=1)\n",
    "    splitted_lengths_batch = lengths.split(split_size=1)\n",
    "\n",
    "    training_data_tuples = zip(splitted_input_sequence_batch,\n",
    "                               splitted_output_sequence_batch,\n",
    "                               splitted_lengths_batch)\n",
    "\n",
    "    training_data_tuples_sorted = sorted(training_data_tuples,\n",
    "                                         key=lambda p: int(p[2]),\n",
    "                                         reverse=True)\n",
    "\n",
    "    splitted_input_sequence_batch, splitted_output_sequence_batch, splitted_lengths_batch = zip(*training_data_tuples_sorted)\n",
    "\n",
    "    input_sequence_batch_sorted = torch.cat(splitted_input_sequence_batch)\n",
    "    output_sequence_batch_sorted = torch.cat(splitted_output_sequence_batch)\n",
    "    lengths_batch_sorted = torch.cat(splitted_lengths_batch)\n",
    "\n",
    "    input_sequence_batch_sorted = input_sequence_batch_sorted[:, :lengths_batch_sorted[0, 0]]\n",
    "    output_sequence_batch_sorted = output_sequence_batch_sorted[:, :lengths_batch_sorted[0, 0]]\n",
    "\n",
    "    input_sequence_batch_transposed = input_sequence_batch_sorted.transpose(0, 1)\n",
    "    \n",
    "    # pytorch's api for rnns wants lenghts to be list of ints\n",
    "    lengths_batch_sorted_list = list(lengths_batch_sorted)\n",
    "    lengths_batch_sorted_list = map(lambda x: int(x), lengths_batch_sorted_list)\n",
    "    \n",
    "    \n",
    "    return input_sequence_batch_transposed, output_sequence_batch_sorted, lengths_batch_sorted_list\n",
    "\n",
    "\n",
    "class RNN(nn.Module):\n",
    "    \n",
    "    def __init__(self, input_size, hidden_size, num_classes, n_layers=2):\n",
    "        \n",
    "        super(RNN, self).__init__()\n",
    "        \n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_classes = num_classes\n",
    "        self.n_layers = n_layers\n",
    "        \n",
    "        # Converts labels into one-hot encoding and runs a linear\n",
    "        # layer on each of the converted one-hot encoded elements\n",
    "        \n",
    "        # input_size -- size of the dictionary + 1 (accounts for padding constant)\n",
    "        self.encoder = nn.Embedding(input_size, hidden_size)\n",
    "        \n",
    "        self.gru = nn.LSTM(hidden_size, hidden_size, n_layers)\n",
    "        \n",
    "        self.logits_fc = nn.Linear(hidden_size, num_classes)\n",
    "    \n",
    "    \n",
    "    def forward(self, input_sequences, input_sequences_lengths, hidden=None):\n",
    "        \n",
    "        batch_size = input_sequences.shape[1]\n",
    "\n",
    "        embedded = self.encoder(input_sequences)\n",
    "\n",
    "        # Here we run rnns only on non-padded regions of the batch\n",
    "        packed = torch.nn.utils.rnn.pack_padded_sequence(embedded, input_sequences_lengths)\n",
    "        outputs, hidden = self.gru(packed, hidden)\n",
    "        outputs, output_lengths = torch.nn.utils.rnn.pad_packed_sequence(outputs) # unpack (back to padded)\n",
    "        \n",
    "        logits = self.logits_fc(outputs)\n",
    "        \n",
    "        logits = logits.transpose(0, 1).contiguous()\n",
    "        \n",
    "        logits_flatten = logits.view(-1, self.num_classes)\n",
    "        \n",
    "        return logits_flatten, hidden\n",
    "\n",
    "    \n",
    "trainset = LyricsGenerationDataset(csv_file_path='songdata.csv')\n",
    "\n",
    "trainset_loader = torch.utils.data.DataLoader(trainset, batch_size=50,\n",
    "                                              shuffle=True, num_workers=4, drop_last=True)\n"
   ]
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   "cell_type": "code",
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   "source": [
    "rnn = RNN_classification(input_size=len(all_characters) + 1, hidden_size=512, num_classes=len(all_characters))\n",
    "rnn.cuda()\n",
    "\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate)\n",
    "criterion = torch.nn.CrossEntropyLoss().cuda()\n",
    "\n",
    "def sample_from_rnn(starting_sting=\"Why\", sample_length=300, temperature=1):\n",
    "\n",
    "    sampled_string = starting_sting\n",
    "    hidden = None\n",
    "\n",
    "    first_input = torch.LongTensor( string_to_labels(starting_sting) ).cuda()\n",
    "    first_input = first_input.unsqueeze(1)\n",
    "    current_input = Variable(first_input)\n",
    "\n",
    "    output, hidden = rnn(current_input, [len(sampled_string)], hidden=hidden)\n",
    "\n",
    "    output = output[-1, :].unsqueeze(0)\n",
    "\n",
    "    for i in xrange(sample_length):\n",
    "\n",
    "        output_dist = nn.functional.softmax( output.view(-1).div(temperature) ).data\n",
    "\n",
    "        predicted_label = torch.multinomial(output_dist, 1)\n",
    "\n",
    "        sampled_string += all_characters[int(predicted_label[0])]\n",
    "\n",
    "        current_input = Variable(predicted_label.unsqueeze(1))\n",
    "\n",
    "        output, hidden = rnn(current_input, [1], hidden=hidden)\n",
    "    \n",
    "    return sampled_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# importing a list that can be binded with a figure and update\n",
    "# the figure when being appended\n",
    "from visualization import VizList\n",
    "\n",
    "# Creating figure, axes and binding to lists \n",
    "f, (loss_axis, validation_axis, train_axis) = plt.subplots(3, 1)\n",
    "\n",
    "loss_axis.plot([], [])\n",
    "validation_axis.plot([], [])\n",
    "train_axis.plot([], [])\n",
    "\n",
    "\n",
    "loss_list = VizList()\n",
    "validation_list = VizList()\n",
    "train_list = VizList()\n",
    "\n",
    "loss_list.bind_to_axis(loss_axis)\n",
    "validation_list.bind_to_axis(validation_axis)\n",
    "train_list.bind_to_axis(train_axis)\n",
    "\n",
    "loss_axis.set_title('Training Loss')\n",
    "validation_axis.set_title('Validation Set Accuracy')\n",
    "train_axis.set_title('Training Set Accuracy')\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why don't you see  \n",
      "  \n",
      "I want to be a star  \n",
      "I want to be a star  \n",
      "I want to be a star  \n",
      "I want to be a\n",
      "Why do you love me  \n",
      "I want to be a long long long long long  \n",
      "I want to be a long long long long long \n",
      "Why do you want to be a little bit of my life  \n",
      "When you want to be a little bit of my life  \n",
      "  \n",
      "I want\n",
      "Why don't you see me so much more  \n",
      "I want to be a stranger  \n",
      "I want to be a stranger  \n",
      "I want to be a \n",
      "Why do I stay away from my heart  \n",
      "When I was a stranger  \n",
      "When I was a stranger  \n",
      "When I was a strange\n",
      "Why do you stay with me?  \n",
      "Where do you go  \n",
      "Where do you go  \n",
      "Where do you go  \n",
      "Where do you go  \n",
      "Wher\n",
      "Why do you stay with me  \n",
      "Why do you stay with me  \n",
      "Why do you stay with me  \n",
      "Why do you stay with me  \n",
      "Why do you see the way you look at me  \n",
      "When you see me with you  \n",
      "When you see me with you  \n",
      "When you \n",
      "Why do I stay with my baby  \n",
      "I want to be with you  \n",
      "I want to be with you  \n",
      "I want to be with you  \n",
      "  \n",
      "Why do you think you were the one to be  \n",
      "When you were the one that we were  \n",
      "When you were the one th\n",
      "Why do you stay with me  \n",
      "When you say you will  \n",
      "When you walk away  \n",
      "When you walk away  \n",
      "When you wa\n",
      "Why do you stay away from me?  \n",
      "I don't know what to do  \n",
      "I don't know what to do  \n",
      "I don't know what t\n",
      "Why do you think you are  \n",
      "When you say you want to  \n",
      "When you say you want to  \n",
      "When you say you want \n",
      "Why do I stay  \n",
      "When I was a stranger  \n",
      "When I was a stranger  \n",
      "When I was a stranger  \n",
      "When I was a st\n",
      "Why do I stay away from you  \n",
      "I don't want to be alone  \n",
      "I don't want to be alone  \n",
      "I don't want to be \n",
      "Why do you have to go  \n",
      "When you got to go  \n",
      "When you got to go  \n",
      "When you got to go  \n",
      "  \n",
      "When you got \n",
      "Why do you come home to me  \n",
      "When you say you love me  \n",
      "When you come home  \n",
      "When you come home  \n",
      "When \n",
      "Why do you stand alone  \n",
      "Why don't you stay  \n",
      "Why don't you stay  \n",
      "Why don't you stay  \n",
      "Why don't you s\n",
      "Why do I have to go  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don\n",
      "Why do you think that you're the one  \n",
      "Who will be the one  \n",
      "Who will be there  \n",
      "When you say that you \n",
      "Why do you hear me  \n",
      "When I was young  \n",
      "When I was a star  \n",
      "When I was a star  \n",
      "When I was a star  \n",
      "Whe\n",
      "Why do you have to go  \n",
      "When you say you want to  \n",
      "When you say you want to  \n",
      "When you want to be the o\n",
      "Why do you think that I can see you  \n",
      "When you think you're gonna love me  \n",
      "When you think you're gonna\n",
      "Why do you have to leave me  \n",
      "I want to be your lover  \n",
      "I want to be your lover  \n",
      "I want to be your lov\n",
      "Why do you have to leave me  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why\n",
      "Why do you hear me say  \n",
      "I want to be with you  \n",
      "I want to be your man  \n",
      "I want to be your man  \n",
      "  \n",
      "I w\n",
      "Why do you care  \n",
      "Why do you cry  \n",
      "Why do you cry  \n",
      "Why do you cry  \n",
      "Why do you cry  \n",
      "Why do you cry  \n",
      "\n",
      "Why do I have to go  \n",
      "When I was young  \n",
      "I was so sure  \n",
      "I was so sure  \n",
      "I was so sure  \n",
      "I was so sure \n",
      "Why do you cry  \n",
      "Why do you cry  \n",
      "Why do you cry  \n",
      "Why do you do  \n",
      "Why do you do  \n",
      "Why do you do  \n",
      "Why \n",
      "Why do I say  \n",
      "Why do I say  \n",
      "Why do I say  \n",
      "Why do I say  \n",
      "Why do I say  \n",
      "Why do I say  \n",
      "Why do I say \n",
      "Why do you come to this  \n",
      "When you say you love me?  \n",
      "Why do you think you're so strong?  \n",
      "Why do you t\n",
      "Why do you have to leave me  \n",
      "I don't want to lose you  \n",
      "I don't want to lose you  \n",
      "I don't want to los\n",
      "Why do you hear the sound of the sun  \n",
      "Where the sun is shining bright  \n",
      "When the sun is shining bright\n",
      "Why do you have to leave me  \n",
      "Why do I feel this way  \n",
      "Why do I feel like I can do  \n",
      "Why do I feel like\n",
      "Why do you have to leave me  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why\n",
      "Why do you have to leave me  \n",
      "When you say you love me  \n",
      "When you say you love me  \n",
      "When you love me  \n",
      "\n",
      "Why do you have to leave me  \n",
      "When you say you love me  \n",
      "When you say you love me  \n",
      "When you love me  \n",
      "\n",
      "Why do I have to leave you alone?  \n",
      "I don't know why I love you  \n",
      "I don't know why I love you  \n",
      "I don't\n",
      "Why do you have to leave me  \n",
      "I don't know why I don't know  \n",
      "I don't know why I don't know  \n",
      "I don't k\n",
      "Why do you have to go  \n",
      "When you say you love me  \n",
      "When you love me  \n",
      "When you love me  \n",
      "When you love \n",
      "Why do you have to leave me  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why  \n",
      "I don't know why\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-171:\n",
      "Process Process-172:\n",
      "Process Process-170:\n",
      "Process Process-169:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
      "    self.run()\n",
      "    self.run()\n",
      "    self.run()\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/site-packages/torch/utils/data/dataloader.py\", line 36, in _worker_loop\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/site-packages/torch/utils/data/dataloader.py\", line 36, in _worker_loop\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/site-packages/torch/utils/data/dataloader.py\", line 36, in _worker_loop\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/site-packages/torch/utils/data/dataloader.py\", line 36, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "    r = index_queue.get()\n",
      "    r = index_queue.get()\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/queues.py\", line 374, in get\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/queues.py\", line 376, in get\n",
      "    racquire()\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/queues.py\", line 374, in get\n",
      "    r = index_queue.get()\n",
      "KeyboardInterrupt\n",
      "    return recv()\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/multiprocessing/queues.py\", line 374, in get\n",
      "    racquire()\n",
      "  File \"/home/daniil/repos/anaconda2/lib/python2.7/site-packages/torch/multiprocessing/queue.py\", line 21, in recv\n",
      "    racquire()\n",
      "KeyboardInterrupt\n",
      "    buf = self.recv_bytes()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-731b70c54413>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0minput_sequences_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_sequences_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequences_lengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpost_processed_batch_tuple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0moutput_sequences_batch_var\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0moutput_sequences_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\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[0m\u001b[1;32m     13\u001b[0m         \u001b[0minput_sequences_batch_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0minput_sequences_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\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     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/daniil/repos/anaconda2/lib/python2.7/site-packages/torch/_utils.pyc\u001b[0m in \u001b[0;36m_cuda\u001b[0;34m(self, device, async)\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0mnew_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "clip = 1.0\n",
    "epochs_number = 1000\n",
    "\n",
    "for epoch_number in xrange(epochs_number):\n",
    "\n",
    "    for batch in trainset_loader:\n",
    "\n",
    "        post_processed_batch_tuple = post_process_sequence_batch(batch)\n",
    "\n",
    "        input_sequences_batch, output_sequences_batch, sequences_lengths = post_processed_batch_tuple\n",
    "\n",
    "        output_sequences_batch_var =  Variable( output_sequences_batch.contiguous().view(-1).cuda() )\n",
    "        input_sequences_batch_var = Variable( input_sequences_batch.cuda() )\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        logits, _ = rnn(input_sequences_batch_var, sequences_lengths)\n",
    "        \n",
    "        loss = criterion(logits, output_sequences_batch_var)\n",
    "        loss_list.append( loss.data[0] )\n",
    "        loss.backward()\n",
    "\n",
    "        #torch.nn.utils.clip_grad_norm(rnn.parameters(), clip)\n",
    "\n",
    "        optimizer.step()\n",
    "        \n",
    "    print(sample_from_rnn())\n",
    "    torch.save(rnn.state_dict(), 'conditional_rnn.pth')\n",
    "    \n",
    "    "
   ]
  }
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