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wip project 3, build_nn

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master
spike 7 years ago
parent dad678a0e3
commit dfa204818e
2 changed files with 234 additions and 40 deletions
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137
tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb
Unescape Escape View File

@ -15,7 +15,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
@ -292,6 +292,35 @@
"int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extra hyper parameters"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import namedtuple\n",
"\n",
"hyper_params = (('embedding_size', 128),\n",
" \n",
" )\n",
"\n",
"\n",
"\n",
"\n",
"Hyper = namedtuple('Hyper', map(lambda x: x[0], hyper_params))\n",
"HYPER = Hyper(*list(map(lambda x: x[1], hyper_params)))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
@ -367,21 +396,37 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def get_inputs():\n",
" \"\"\"\n",
" Create TF Placeholders for input, targets, and learning rate.\n",
" :return: Tuple (input, targets, learning rate)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None, None, None\n",
" \n",
" # We use shape [None, None] to feed any batch size and any sequence length\n",
" input_placeholder = tf.placeholder(tf.int32, [None, None],name='input')\n",
" \n",
" # Targets are [batch_size, seq_length]\n",
" targets_placeholder = tf.placeholder(tf.int32, [None, None]) \n",
" \n",
" \n",
" learning_rate_placeholder = tf.placeholder(tf.float32)\n",
" return input_placeholder, targets_placeholder, learning_rate_placeholder\n",
"\n",
"\n",
"\"\"\"\n",
@ -408,13 +453,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def get_init_cell(batch_size, rnn_size):\n",
" \"\"\"\n",
@ -423,8 +476,14 @@
" :param rnn_size: Size of RNNs\n",
" :return: Tuple (cell, initialize state)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None, None\n",
" lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n",
" \n",
" cell = tf.contrib.rnn.MultiRNNCell([lstm] * 2)\n",
" \n",
" initial_state = cell.zero_state(batch_size, tf.float32)\n",
" initial_state = tf.identity(initial_state, name='initial_state')\n",
" \n",
" return cell, initial_state\n",
"\n",
"\n",
"\"\"\"\n",
@ -446,13 +505,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def get_embed(input_data, vocab_size, embed_dim):\n",
" \"\"\"\n",
@ -462,8 +529,13 @@
" :param embed_dim: Number of embedding dimensions\n",
" :return: Embedded input.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None\n",
" embeddings = tf.Variable(\n",
" tf.random_uniform([vocab_size, embed_dim], -1.0, 1.0)\n",
" )\n",
" \n",
" embed = tf.nn.embedding_lookup(embeddings, input_data)\n",
" \n",
" return embed\n",
"\n",
"\n",
"\"\"\"\n",
@ -489,13 +561,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def build_rnn(cell, inputs):\n",
" \"\"\"\n",
@ -504,8 +584,11 @@
" :param inputs: Input text data\n",
" :return: Tuple (Outputs, Final State)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None, None\n",
" outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)\n",
" final_state = tf.identity(final_state, name='final_state')\n",
" \n",
" \n",
" return outputs, final_state\n",
"\n",
"\n",
"\"\"\"\n",
@ -532,13 +615,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 37,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"ename": "AssertionError",
"evalue": "Final state doesn't have the \"name\" attribute. Are you using build_rnn?",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-73c6b69893b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mDON\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mT\u001b[0m \u001b[0mMODIFY\u001b[0m \u001b[0mANYTHING\u001b[0m \u001b[0mIN\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mCELL\u001b[0m \u001b[0mTHAT\u001b[0m \u001b[0mIS\u001b[0m \u001b[0mBELOW\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mLINE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \"\"\"\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_build_nn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuild_nn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/spike/ml/udacity/nd101/deep-learning-modified/tv-script-generation/problem_unittests.py\u001b[0m in \u001b[0;36mtest_build_nn\u001b[0;34m(build_nn)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0;31m# Check name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'name'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m \u001b[0;34m'Final state doesn\\'t have the \"name\" attribute. Are you using build_rnn?'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 245\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mfinal_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'final_state:0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m'Final state doesn\\'t have the correct name. Found the name {}. Are you using build_rnn?'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: Final state doesn't have the \"name\" attribute. Are you using build_rnn?"
]
}
],
"source": [
"def build_nn(cell, rnn_size, input_data, vocab_size):\n",
" \"\"\"\n",
@ -549,7 +645,8 @@
" :param vocab_size: Vocabulary size\n",
" :return: Tuple (Logits, FinalState)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" embed = get_embed(input_data, vocab_size, HYPER.embedding_size)\n",
" \n",
" return None, None\n",
"\n",
"\n",

137
tv-script-generation/dlnd_tv_script_generation.ipynb
Unescape Escape View File

@ -15,7 +15,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
@ -292,6 +292,35 @@
"int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extra hyper parameters"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import namedtuple\n",
"\n",
"hyper_params = (('embedding_size', 128),\n",
" \n",
" )\n",
"\n",
"\n",
"\n",
"\n",
"Hyper = namedtuple('Hyper', map(lambda x: x[0], hyper_params))\n",
"HYPER = Hyper(*list(map(lambda x: x[1], hyper_params)))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
@ -367,21 +396,37 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def get_inputs():\n",
" \"\"\"\n",
" Create TF Placeholders for input, targets, and learning rate.\n",
" :return: Tuple (input, targets, learning rate)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None, None, None\n",
" \n",
" # We use shape [None, None] to feed any batch size and any sequence length\n",
" input_placeholder = tf.placeholder(tf.int32, [None, None],name='input')\n",
" \n",
" # Targets are [batch_size, seq_length]\n",
" targets_placeholder = tf.placeholder(tf.int32, [None, None]) \n",
" \n",
" \n",
" learning_rate_placeholder = tf.placeholder(tf.float32)\n",
" return input_placeholder, targets_placeholder, learning_rate_placeholder\n",
"\n",
"\n",
"\"\"\"\n",
@ -408,13 +453,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def get_init_cell(batch_size, rnn_size):\n",
" \"\"\"\n",
@ -423,8 +476,14 @@
" :param rnn_size: Size of RNNs\n",
" :return: Tuple (cell, initialize state)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None, None\n",
" lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n",
" \n",
" cell = tf.contrib.rnn.MultiRNNCell([lstm] * 2)\n",
" \n",
" initial_state = cell.zero_state(batch_size, tf.float32)\n",
" initial_state = tf.identity(initial_state, name='initial_state')\n",
" \n",
" return cell, initial_state\n",
"\n",
"\n",
"\"\"\"\n",
@ -446,13 +505,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def get_embed(input_data, vocab_size, embed_dim):\n",
" \"\"\"\n",
@ -462,8 +529,13 @@
" :param embed_dim: Number of embedding dimensions\n",
" :return: Embedded input.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None\n",
" embeddings = tf.Variable(\n",
" tf.random_uniform([vocab_size, embed_dim], -1.0, 1.0)\n",
" )\n",
" \n",
" embed = tf.nn.embedding_lookup(embeddings, input_data)\n",
" \n",
" return embed\n",
"\n",
"\n",
"\"\"\"\n",
@ -489,13 +561,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def build_rnn(cell, inputs):\n",
" \"\"\"\n",
@ -504,8 +584,11 @@
" :param inputs: Input text data\n",
" :return: Tuple (Outputs, Final State)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None, None\n",
" outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)\n",
" final_state = tf.identity(final_state, name='final_state')\n",
" \n",
" \n",
" return outputs, final_state\n",
"\n",
"\n",
"\"\"\"\n",
@ -532,13 +615,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 37,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"outputs": [
{
"ename": "AssertionError",
"evalue": "Final state doesn't have the \"name\" attribute. Are you using build_rnn?",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-73c6b69893b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mDON\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mT\u001b[0m \u001b[0mMODIFY\u001b[0m \u001b[0mANYTHING\u001b[0m \u001b[0mIN\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mCELL\u001b[0m \u001b[0mTHAT\u001b[0m \u001b[0mIS\u001b[0m \u001b[0mBELOW\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mLINE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \"\"\"\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_build_nn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuild_nn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/spike/ml/udacity/nd101/deep-learning-modified/tv-script-generation/problem_unittests.py\u001b[0m in \u001b[0;36mtest_build_nn\u001b[0;34m(build_nn)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0;31m# Check name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'name'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m \u001b[0;34m'Final state doesn\\'t have the \"name\" attribute. Are you using build_rnn?'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 245\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mfinal_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'final_state:0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m'Final state doesn\\'t have the correct name. Found the name {}. Are you using build_rnn?'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: Final state doesn't have the \"name\" attribute. Are you using build_rnn?"
]
}
],
"source": [
"def build_nn(cell, rnn_size, input_data, vocab_size):\n",
" \"\"\"\n",
@ -549,7 +645,8 @@
" :param vocab_size: Vocabulary size\n",
" :return: Tuple (Logits, FinalState)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" embed = get_embed(input_data, vocab_size, HYPER.embedding_size)\n",
" \n",
" return None, None\n",
"\n",
"\n",

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