}
},
"source": [
- "# Case study: building an RNN\n"
+ "# Case study: training a custom RNN, using Keras and Estimators\n"
]
},
{
" length = tf.cast(tf.shape(chars)[0], dtype=tf.int64)\n",
" return rgb, chars, length\n",
"\n",
+ "\n",
+ "def set_static_batch_shape(batch_size):\n",
+ " def apply(rgb, chars, length):\n",
+ " rgb.set_shape((batch_size, None))\n",
+ " chars.set_shape((batch_size, None, 256))\n",
+ " length.set_shape((batch_size,))\n",
+ " return rgb, chars, length\n",
+ " return apply\n",
+ "\n",
+ "\n",
"def load_dataset(data_dir, url, batch_size, training=True):\n",
" \"\"\"Loads the colors data at path into a tf.PaddedDataset.\"\"\"\n",
" path = tf.keras.utils.get_file(os.path.basename(url), url, cache_dir=data_dir)\n",
" if training:\n",
" dataset = dataset.shuffle(buffer_size=3000)\n",
" dataset = dataset.padded_batch(\n",
- " batch_size, padded_shapes=([None], [None, None], []))\n",
+ " batch_size, padded_shapes=((None,), (None, 256), ()))\n",
+ " # To simplify the model code, we statically set as many of the shapes that we\n",
+ " # know.\n",
+ " dataset = dataset.map(set_static_batch_shape(batch_size))\n",
" return dataset"
]
},
"source": [
"To show the use of control flow, we write the RNN loop by hand, rather than using a pre-built RNN model.\n",
"\n",
- "Note how we write the model code in Eager style, with regular `if` and `while` statements. Then, we annotate the functions with `@autograph.convert` to have them automatically compiled to run in graph mode."
+ "Note how we write the model code in Eager style, with regular `if` and `while` statements. Then, we annotate the functions with `@autograph.convert` to have them automatically compiled to run in graph mode.\n",
+ "We use Keras to define the model, and we will train it using Estimators."
]
},
{
},
"outputs": [],
"source": [
- "class RnnColorbot(object):\n",
- " \"\"\"Holds the parameters of the colorbot model.\"\"\"\n",
+ "@autograph.convert()\n",
+ "class RnnColorbot(tf.keras.Model):\n",
+ " \"\"\"RNN Colorbot model.\"\"\"\n",
"\n",
" def __init__(self):\n",
+ " super(RnnColorbot, self).__init__()\n",
" self.lower_cell = tf.contrib.rnn.LSTMBlockCell(256)\n",
" self.upper_cell = tf.contrib.rnn.LSTMBlockCell(128)\n",
" self.relu_layer = tf.layers.Dense(3, activation=tf.nn.relu)\n",
"\n",
+ "\n",
+ " def _rnn_layer(self, chars, cell, batch_size, training):\n",
+ " \"\"\"A single RNN layer.\n",
+ "\n",
+ " Args:\n",
+ " chars: A Tensor of shape (max_sequence_length, batch_size, input_size)\n",
+ " cell: An object of type tf.contrib.rnn.LSTMBlockCell\n",
+ " batch_size: Int, the batch size to use\n",
+ " training: Boolean, whether the layer is used for training\n",
+ "\n",
+ " Returns:\n",
+ " A Tensor of shape (max_sequence_length, batch_size, output_size).\n",
+ " \"\"\"\n",
+ " hidden_outputs = []\n",
+ " autograph.utils.set_element_type(hidden_outputs, tf.float32)\n",
+ " state, output = cell.zero_state(batch_size, tf.float32)\n",
+ " for ch in chars:\n",
+ " cell_output, (state, output) = cell.call(ch, (state, output))\n",
+ " hidden_outputs.append(cell_output)\n",
+ " hidden_outputs = hidden_outputs.stack()\n",
+ " if training:\n",
+ " hidden_outputs = tf.nn.dropout(hidden_outputs, 0.5)\n",
+ " return hidden_outputs\n",
+ "\n",
+ " def build(self, _):\n",
+ " \"\"\"Creates the model variables. See keras.Model.build().\"\"\"\n",
" self.lower_cell.build(tf.TensorShape((None, 256)))\n",
" self.upper_cell.build(tf.TensorShape((None, 256)))\n",
- " self.relu_layer.build(tf.TensorShape((None, 128)))\n",
+ " self.relu_layer.build(tf.TensorShape((None, 128))) \n",
+ " self.built = True\n",
"\n",
"\n",
- "def rnn_layer(chars, cell, batch_size, training):\n",
- " \"\"\"A simple RNN layer.\n",
- " \n",
- " Args:\n",
- " chars: A Tensor of shape (max_sequence_length, batch_size, input_size)\n",
- " cell: An object of type tf.contrib.rnn.LSTMBlockCell\n",
- " batch_size: Int, the batch size to use\n",
- " training: Boolean, whether the layer is used for training\n",
+ " def call(self, inputs, training=False):\n",
+ " \"\"\"The RNN model code. Uses Eager and \n",
"\n",
- " Returns:\n",
- " A Tensor of shape (max_sequence_length, batch_size, output_size).\n",
- " \"\"\"\n",
- " hidden_outputs = []\n",
- " autograph.utils.set_element_type(hidden_outputs, tf.float32)\n",
- " state, output = cell.zero_state(batch_size, tf.float32)\n",
- " for ch in chars:\n",
- " cell_output, (state, output) = cell.call(ch, (state, output))\n",
- " hidden_outputs.append(cell_output)\n",
- " hidden_outputs = hidden_outputs.stack()\n",
- " if training:\n",
- " hidden_outputs = tf.nn.dropout(hidden_outputs, 0.5)\n",
- " return hidden_outputs\n",
+ " The model consists of two RNN layers (made by lower_cell and upper_cell),\n",
+ " followed by a fully connected layer with ReLU activation.\n",
"\n",
+ " Args:\n",
+ " inputs: A tuple (chars, length)\n",
+ " training: Boolean, whether the layer is used for training\n",
"\n",
- "@autograph.convert(recursive=True)\n",
- "def model(inputs, colorbot, batch_size, training):\n",
- " \"\"\"RNNColorbot model.\n",
- " \n",
- " The model consists of two RNN layers (made by lower_cell and upper_cell),\n",
- " followed by a fully connected layer with ReLU activation.\n",
- " \n",
- " Args:\n",
- " inputs: A tuple (chars, length)\n",
- " colorbot: An object of type RnnColorbot\n",
- " batch_size: Int, the batch size to use\n",
- " training: Boolean, whether the layer is used for training\n",
- " \n",
- " Returns:\n",
- " A Tensor of shape (batch_size, 3) - the model predictions.\n",
- " \"\"\"\n",
- " (chars, length) = inputs\n",
- " seq = tf.transpose(chars, [1, 0, 2])\n",
- " seq.set_shape((None, batch_size, 256))\n",
+ " Returns:\n",
+ " A Tensor of shape (batch_size, 3) - the model predictions.\n",
+ " \"\"\"\n",
+ " chars, length = inputs\n",
+ " batch_size = chars.shape[0]\n",
+ " seq = tf.transpose(chars, (1, 0, 2))\n",
"\n",
- " seq = rnn_layer(seq, colorbot.lower_cell, batch_size, training)\n",
- " seq = rnn_layer(seq, colorbot.upper_cell, batch_size, training)\n",
+ " seq = self._rnn_layer(seq, self.lower_cell, batch_size, training)\n",
+ " seq = self._rnn_layer(seq, self.upper_cell, batch_size, training)\n",
"\n",
- " # Grab just the end-of-sequence from each output.\n",
- " indices = tf.stack([length - 1, range(batch_size)], axis=1)\n",
- " sequence_ends = tf.gather_nd(seq, indices)\n",
- " return colorbot.relu_layer(sequence_ends)\n",
+ " # Grab just the end-of-sequence from each output.\n",
+ " indices = tf.stack([length - 1, range(batch_size)], axis=1)\n",
+ " sequence_ends = tf.gather_nd(seq, indices)\n",
+ " return self.relu_layer(sequence_ends)\n",
"\n",
"@autograph.convert()\n",
"def loss_fn(labels, predictions):\n",
}
},
"source": [
- "We will now create the model function for the estimator.\n",
+ "We will now create the model function for the custom Estimator.\n",
"\n",
- "In the model function, we simply call the converted functions that we defined above - that's it!"
+ "In the model function, we simply use the model class we defined above - that's it!"
]
},
{
" sequence_length = features['sequence_length']\n",
" inputs = (chars, sequence_length)\n",
"\n",
- " # Create the model components.\n",
- " # Simply calling the AutoGraph-ed functions and objects just works!\n",
+ " # Create the model. Simply using the AutoGraph-ed class just works!\n",
" colorbot = RnnColorbot()\n",
- " \n",
- " batch_size = params['batch_size']\n",
+ " colorbot.build(None)\n",
"\n",
" if mode == tf.estimator.ModeKeys.TRAIN:\n",
- " predictions = model(inputs, colorbot, batch_size, training=True)\n",
+ " predictions = colorbot(inputs, training=True)\n",
" loss = loss_fn(labels, predictions)\n",
"\n",
" learning_rate = params['learning_rate']\n",
" return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)\n",
"\n",
" elif mode == tf.estimator.ModeKeys.EVAL:\n",
- " predictions = model(inputs, colorbot, batch_size, training=False)\n",
+ " predictions = colorbot(inputs)\n",
" loss = loss_fn(labels, predictions)\n",
"\n",
" return tf.estimator.EstimatorSpec(mode, loss=loss)\n",
- " \n",
+ "\n",
" elif mode == tf.estimator.ModeKeys.PREDICT:\n",
- " # For prediction, we expect single tensors.\n",
- " predictions = model(inputs, colorbot, 1, training=False)\n",
+ " predictions = colorbot(inputs)\n",
"\n",
" predictions = tf.minimum(predictions, 1.0)\n",
" return tf.estimator.EstimatorSpec(mode, predictions=predictions)"
},
{
"cell_type": "code",
- "execution_count": 0,
+ "execution_count": 7,
"metadata": {
"colab": {
"autoexec": {
},
"colab_type": "code",
"executionInfo": {
- "elapsed": 10064,
+ "elapsed": 10604,
"status": "ok",
- "timestamp": 1523580419240,
+ "timestamp": 1524095272039,
"user": {
"displayName": "",
"photoUrl": "",
"user_tz": 240
},
"id": "2pg1AfbxBJQq",
- "outputId": "41894b16-3d3a-4e30-f6e4-5a9c837a2210",
+ "outputId": "9c924b4f-06e1-4538-976c-a3e1ddac5660",
"slideshow": {
"slide_type": "-"
}
"name": "stdout",
"output_type": "stream",
"text": [
- "Eval loss at step 100: 0.0665446\n"
+ "Eval loss at step 100: 0.0674834\n"
]
}
],
},
{
"cell_type": "code",
- "execution_count": 0,
+ "execution_count": 8,
"metadata": {
"colab": {
"autoexec": {
},
"colab_type": "code",
"executionInfo": {
- "elapsed": 31286,
+ "elapsed": 7990,
"status": "ok",
- "timestamp": 1523580450579,
+ "timestamp": 1524095280105,
"user": {
"displayName": "",
"photoUrl": "",
"user_tz": 240
},
"id": "dxHex2tUN_10",
- "outputId": "b3dc558d-b800-4e9b-e60e-3441124e80d8",
+ "outputId": "2b889e5a-b9ed-4645-bf03-d98f26c72101",
"slideshow": {
"slide_type": "slide"
}
"\u003clink rel=stylesheet type=text/css href='/nbextensions/google.colab/tabbar.css'\u003e\u003c/link\u003e"
],
"text/plain": [
- "\u003cIPython.core.display.HTML at 0x7f4112527e90\u003e"
+ "\u003cIPython.core.display.HTML at 0x7f3f36aa6cd0\u003e"
]
},
"metadata": {
"\u003cscript src='/nbextensions/google.colab/tabbar_main.min.js'\u003e\u003c/script\u003e"
],
"text/plain": [
- "\u003cIPython.core.display.HTML at 0x7f4112527f10\u003e"
+ "\u003cIPython.core.display.HTML at 0x7f3eca67f7d0\u003e"
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"metadata": {
"\u003cdiv id=\"id1\"\u003e\u003c/div\u003e"
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"text/plain": [
- "\u003cIPython.core.display.HTML at 0x7f4112527f50\u003e"
+ "\u003cIPython.core.display.HTML at 0x7f3eca67f8d0\u003e"
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- "//# sourceURL=js_a0db480422"
+ "window[\"e8ddfa22-4362-11e8-91ec-c8d3ffb5fbe0\"] = colab_lib.createTabBar({\"contentBorder\": [\"0px\"], \"elementId\": \"id1\", \"borderColor\": [\"#a7a7a7\"], \"contentHeight\": [\"initial\"], \"tabNames\": [\"RNN Colorbot\"], \"location\": \"top\", \"initialSelection\": 0});\n",
+ "//# sourceURL=js_71b9087b6d"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f410f8fd1d0\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3eca67f950\u003e"
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- "//# sourceURL=js_d2a46ea291"
+ "window[\"e8ddfa23-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n",
+ "//# sourceURL=js_e390445f33"
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- "window[\"2c60f476-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n",
- "//# sourceURL=js_0a8262c6e9"
+ "window[\"e8ddfa24-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n",
+ "//# sourceURL=js_241dd76d85"
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- "//# sourceURL=js_e32f85ccd2"
+ "window[\"e8ddfa25-4362-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n",
+ "//# sourceURL=js_60c64e3d50"
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- "//# sourceURL=js_eaee748b21"
+ "window[\"e8ddfa26-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"e8ddfa25-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n",
+ "//# sourceURL=js_14ea437cbd"
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- "//# sourceURL=js_2befe06587"
+ "window[\"e8ddfa27-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n",
+ "//# sourceURL=js_09294c2226"
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"text/plain": [
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- "//# sourceURL=js_8ec4aeeb25"
+ "window[\"ec965514-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"e8ddfa24-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n",
+ "//# sourceURL=js_e5e8266997"
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"application/javascript": [
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- "//# sourceURL=js_9f9f4574f1"
+ "window[\"ec965515-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n",
+ "//# sourceURL=js_07a097f0ee"
],
"text/plain": [
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- "//# sourceURL=js_bcccd8f300"
+ "window[\"ec965516-4362-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n",
+ "//# sourceURL=js_790d669ca8"
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"data": {
"application/javascript": [
- "window[\"354d7b1d-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"354d7b1c-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
- "//# sourceURL=js_2c056cee72"
+ "window[\"ec965517-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"ec965516-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n",
+ "//# sourceURL=js_d30df771f0"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f410f8fd490\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3eca67fd90\u003e"
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"data": {
"application/javascript": [
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- "//# sourceURL=js_c853c3f58b"
+ "window[\"ec965518-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n",
+ "//# sourceURL=js_8a43a2da4b"
],
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+ "\u003cIPython.core.display.Javascript at 0x7f3eca67fc50\u003e"
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{
"data": {
- "application/javascript": [
- "window[\"354d7b1f-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"354d7b1b-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
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- "//# sourceURL=js_831db7458f"
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- "//# sourceURL=js_adb576c6eb"
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- "//# sourceURL=js_460b91ad4a"
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- "//# sourceURL=js_7dedd0b037"
- ],
- "text/plain": [
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- "window[\"3803fabc-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"3803fabb-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
- "//# sourceURL=js_d64fedfcf9"
- ],
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- "window[\"3803fabd-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n",
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- "data": {
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- "window[\"3b9b986c-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"3803faba-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
- "//# sourceURL=js_9f9cf2b76f"
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- "data": {
- "application/javascript": [
- "window[\"3b9b986d-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n",
- "//# sourceURL=js_b402e6b587"
- ],
- "text/plain": [
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- "window[\"3b9b986e-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n",
- "//# sourceURL=js_9b7d66db72"
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- "data": {
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- "window[\"3b9b986f-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"3b9b986e-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
- "//# sourceURL=js_11ec213a3f"
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- "window[\"3b9b9870-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n",
- "//# sourceURL=js_9c055e4bc0"
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- "text/plain": [
- "\u003cmatplotlib.figure.Figure at 0x7f4113124310\u003e"
+ "\u003cmatplotlib.figure.Figure at 0x7f3ecc00bf10\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3b9b9871-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"3b9b986d-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
- "//# sourceURL=js_ba6a061307"
+ "window[\"ec965519-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"ec965515-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n",
+ "//# sourceURL=js_893ad561f4"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f410f8fd890\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31b55c90\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3b9b9872-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n",
- "//# sourceURL=js_83e3496927"
+ "window[\"ec96551a-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n",
+ "//# sourceURL=js_2d99e0ac17"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f410f8fd590\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3eca67fe50\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3b9b9873-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n",
- "//# sourceURL=js_f437bab20d"
+ "window[\"ec96551b-4362-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n",
+ "//# sourceURL=js_5c19462e32"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f41127a22d0\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31b55dd0\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3b9b9874-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"3b9b9873-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
- "//# sourceURL=js_93aa63450e"
+ "window[\"ec96551c-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"ec96551b-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n",
+ "//# sourceURL=js_b9c8b7567b"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f41127a2b90\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31b55a50\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3b9b9875-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n",
- "//# sourceURL=js_aca189bea5"
+ "window[\"ec96551d-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n",
+ "//# sourceURL=js_fd05186348"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f410f8fd4d0\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31b55810\u003e"
]
},
"metadata": {
{
"data": {
"text/html": [
- "\u003cdiv class=id_100313201 style=\"margin-right:10px; display:flex;align-items:center;\"\u003e\u003cspan style=\"margin-right: 3px;\"\u003e\u003c/span\u003e\u003c/div\u003e"
+ "\u003cdiv class=id_888646481 style=\"margin-right:10px; display:flex;align-items:center;\"\u003e\u003cspan style=\"margin-right: 3px;\"\u003e\u003c/span\u003e\u003c/div\u003e"
],
"text/plain": [
- "\u003cIPython.core.display.HTML at 0x7f410f990a90\u003e"
+ "\u003cIPython.core.display.HTML at 0x7f3f32414810\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3b9b9876-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_100313201 span\");\n",
- "//# sourceURL=js_5df1fe383e"
+ "window[\"ec96551e-4362-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_888646481 span\");\n",
+ "//# sourceURL=js_efef96e882"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f410f8fd490\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31b55710\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3b9b9877-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = window[\"3b9b9876-3eb4-11e8-91ec-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n",
- "//# sourceURL=js_c62c7174ad"
+ "window[\"ec96551f-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"ec96551e-4362-11e8-91ec-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n",
+ "//# sourceURL=js_6eca889864"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f41127a2390\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3eca67f990\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3ed76584-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_100313201 input\");\n",
- "//# sourceURL=js_2e2201ddc4"
+ "window[\"ed8ea972-4362-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_888646481 input\");\n",
+ "//# sourceURL=js_f02070cc60"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f41127a2810\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31b553d0\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3ed76585-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = window[\"3ed76584-3eb4-11e8-91ec-c8d3ffb5fbe0\"].remove();\n",
- "//# sourceURL=js_288e5283d6"
+ "window[\"ed8ea973-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"ed8ea972-4362-11e8-91ec-c8d3ffb5fbe0\"].remove();\n",
+ "//# sourceURL=js_ed9faba660"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f41127a26d0\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31a95450\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3ed76586-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_100313201 span\");\n",
- "//# sourceURL=js_2f31d19cde"
+ "window[\"ed8ea974-4362-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_888646481 span\");\n",
+ "//# sourceURL=js_f3458d7074"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f41127a2fd0\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31a95250\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3ed76587-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = window[\"3ed76586-3eb4-11e8-91ec-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n",
- "//# sourceURL=js_2fbbcda050"
+ "window[\"ed8ea975-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"ed8ea974-4362-11e8-91ec-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n",
+ "//# sourceURL=js_3ffd97bd6f"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f4112527e90\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31a953d0\u003e"
]
},
"metadata": {
{
"data": {
"application/javascript": [
- "window[\"3ed76588-3eb4-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"3b9b9872-3eb4-11e8-91ec-c8d3ffb5fbe0\"]);\n",
- "//# sourceURL=js_f94d975cf3"
+ "window[\"ed8ea976-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"ec96551a-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n",
+ "//# sourceURL=js_7f73e8bcca"
],
"text/plain": [
- "\u003cIPython.core.display.Javascript at 0x7f41127a2fd0\u003e"
+ "\u003cIPython.core.display.Javascript at 0x7f3f31b55710\u003e"
]
},
"metadata": {
"def predict_input_fn(color_name):\n",
" \"\"\"An input function for prediction.\"\"\"\n",
" _, chars, sequence_length = parse(color_name)\n",
- " \n",
+ "\n",
" # We create a batch of a single element.\n",
" features = {\n",
" 'chars': tf.expand_dims(chars, 0),\n",
"colab": {
"collapsed_sections": [],
"default_view": {},
- "name": "RNN Colorbot using Estimators",
+ "last_runtime": {
+ "build_target": "",
+ "kind": "local"
+ },
+ "name": "RNN Colorbot using Keras and Estimators",
"provenance": [
{
"file_id": "1CtzefX39ffFibX_BqE6cRbT0UW_DdVKl",
projects / platform / upstream / tensorflow.git / commitdiff