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vicpara/Save->Load data from TFRecordDataset.ipynb Secret

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Saving and loading records into/from a TFRecordDataset
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Save->Load data from TFRecordDataset.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple model that works"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_2\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_4 (Dense) (None, 7, 100) 1000 \n",
"_________________________________________________________________\n",
"flatten_2 (Flatten) (None, 700) 0 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 3) 2103 \n",
"=================================================================\n",
"Total params: 3,103\n",
"Trainable params: 3,103\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"Epoch 1/2\n",
"10/10 - 0s - loss: 3.7342\n",
"Epoch 2/2\n",
"10/10 - 0s - loss: 3.4431\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import random\n",
"import tensorflow as tf\n",
"import time as tm\n",
"\n",
"INPUT_SHAPE=[7, 9]\n",
"NUM_POINTS=160\n",
"BATCH_SIZE=16\n",
"BUFFER_SIZE=32\n",
"EPOCHS=2\n",
"\n",
"def gen(num, INPUT_SHAPE):\n",
" for i in range(num):\n",
" x = np.random.rand(INPUT_SHAPE[0], INPUT_SHAPE[1])\n",
" y = random.randint(0, 2)\n",
" yield x,y\n",
" \n",
"orig = tf.data.Dataset.from_generator(\n",
" generator=gen, \n",
" output_types=(tf.float32, tf.float32),\n",
" output_shapes=([None, INPUT_SHAPE[1]], ()),\n",
" args=([NUM_POINTS, INPUT_SHAPE])\n",
").batch(BATCH_SIZE)\n",
"\n",
"def create_model(input_shape):\n",
" model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(100, activation=\"tanh\", input_shape=input_shape),\n",
" tf.keras.layers.Flatten(),\n",
" tf.keras.layers.Dense(3, activation=\"softmax\")\n",
" ])\n",
" return model\n",
"\n",
"model = create_model(input_shape=INPUT_SHAPE)\n",
"model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4, clipvalue=1.0),\n",
" loss= tf.keras.losses.CategoricalCrossentropy(),\n",
")\n",
"print(model.summary())\n",
"hist = model.fit(orig, epochs=EPOCHS, verbose=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset -> TFRecord --> TFRecordDataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def _tensor_to_feature(tensor):\n",
" serialized_tensor = tf.io.serialize_tensor(tensor)\n",
" bytes_string = serialized_tensor.numpy()\n",
" bytes_list = tf.train.BytesList(value=[bytes_string])\n",
" return tf.train.Feature(bytes_list=bytes_list)\n",
"\n",
"def _int64_feature(value):\n",
" return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n",
"\n",
"def serialize_datum(x, y):\n",
" feature = {\n",
" 'x': _tensor_to_feature(x),\n",
" 'y': _tensor_to_feature(y),\n",
" }\n",
" fs = tf.train.Features(feature=feature)\n",
" return tf.train.Example(features=fs).SerializeToString()\n",
"\n",
"@tf.function\n",
"def tf_to_string(x, y):\n",
" res = tf.py_function(\n",
" serialize_datum,\n",
" (x, y),\n",
" tf.string)\n",
" return res\n",
"\n",
"feature_description = {\n",
" 'x': tf.io.FixedLenFeature([], tf.string, default_value=''),\n",
" 'y': tf.io.FixedLenFeature([], tf.string, default_value=''),\n",
"}\n",
"\n",
"@tf.function\n",
"def _parse_datum(datum):\n",
" res = tf.io.parse_single_example(datum, feature_description)\n",
" x = tf.io.parse_tensor(tf.convert_to_tensor(res['x']), out_type=tf.float32)\n",
" y = tf.io.parse_tensor(tf.convert_to_tensor(res['y']), out_type=tf.float32)\n",
" return x,y\n",
"\n",
"def save_n_load(orig_ds, filename = 'test.tfrecord'):\n",
" transf_ds = orig_ds.map(tf_to_string)\n",
" writer = tf.io.TFRecordWriter(filename,\n",
" options = tf.io.TFRecordOptions(\n",
" compression_type=\"GZIP\"\n",
" ))\n",
" for d in transf_ds:\n",
" writer.write(d.numpy())\n",
" writer.flush()\n",
" writer.close()\n",
" \n",
" rawds = tf.data.TFRecordDataset([filename], compression_type=\"GZIP\")\n",
" read_ds = rawds.map(_parse_datum)\n",
" print(orig_ds)\n",
" print(read_ds)\n",
" print(\" ~~~~~~~~~~ \")\n",
" for x,y in orig_ds:\n",
" print(y.shape)\n",
" print(x.shape)\n",
" break\n",
"\n",
" print(\" ~~~~~~~~~~ \")\n",
" for x,y in read_ds:\n",
" print(y.shape)\n",
" print(x.shape)\n",
" break\n",
" \n",
" return read_ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training with the reconstructed dataset via **TFRecordDataset**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<DatasetV1Adapter shapes: ((None, None, 9), (None,)), types: (tf.float32, tf.float32)>\n",
"<MapDataset shapes: (<unknown>, <unknown>), types: (tf.float32, tf.float32)>\n",
" ~~~~~~~~~~ \n",
"(16,)\n",
"(16, 7, 9)\n",
" ~~~~~~~~~~ \n",
"(16,)\n",
"(16, 7, 9)\n",
"Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_6 (Dense) (None, 7, 100) 1000 \n",
"_________________________________________________________________\n",
"flatten_3 (Flatten) (None, 700) 0 \n",
"_________________________________________________________________\n",
"dense_7 (Dense) (None, 3) 2103 \n",
"=================================================================\n",
"Total params: 3,103\n",
"Trainable params: 3,103\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"Epoch 1/2\n"
]
},
{
"ename": "ValueError",
"evalue": "as_list() is not defined on an unknown TensorShape.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-e0d1f7989da4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mread_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 727\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m 729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 730\u001b[0m def evaluate(self,\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0mtraining_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m total_epochs=epochs)\n\u001b[0m\u001b[1;32m 325\u001b[0m \u001b[0mcbks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[0;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[1;32m 121\u001b[0m step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[1;32m 122\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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 124\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\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 125\u001b[0m \u001b[0;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[0;34m(input_fn)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m return nest.map_structure(_non_none_constant_value,\n\u001b[0;32m---> 86\u001b[0;31m distributed_function(input_fn))\n\u001b[0m\u001b[1;32m 87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 456\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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 458\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\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 459\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_counter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalled_without_tracing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0minitializer_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobject_identity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mObjectIdentityDictionary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializer_map\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 504\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m 406\u001b[0m self._concrete_stateful_fn = (\n\u001b[1;32m 407\u001b[0m self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access\n\u001b[0;32m--> 408\u001b[0;31m *args, **kwds))\n\u001b[0m\u001b[1;32m 409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\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",
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"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mdistributed_function\u001b[0;34m(input_iterator)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\"\"\"A single step of the distributed execution across replicas.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m x, y, sample_weights = _prepare_feed_values(\n\u001b[0;32m---> 66\u001b[0;31m model, input_iterator, mode)\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;31m# Call `Model.{train,test,predict}_on_batch` on every replica passing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# PerReplicas as arguments. On every replica inside this call, each\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36m_prepare_feed_values\u001b[0;34m(model, inputs, mode)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0minputs\u001b[0m \u001b[0mwill\u001b[0m \u001b[0malways\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mwrapped\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlists\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \"\"\"\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_input_from_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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 113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;31m# When the inputs are dict, then we want to flatten it in the same order as\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36m_get_input_from_iterator\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;31m# Validate that all the elements in x and y are of the same type and shape.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m dist_utils.validate_distributed_dataset_inputs(\n\u001b[0;32m--> 149\u001b[0;31m distribution_strategy_context.get_strategy(), x, y, sample_weights)\n\u001b[0m\u001b[1;32m 150\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py\u001b[0m in \u001b[0;36mvalidate_distributed_dataset_inputs\u001b[0;34m(distribution_strategy, x, y, sample_weights)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0;31m# If each element of x and y are not tensors, we cannot standardize and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0;31m# validate the input and targets.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m \u001b[0mx_values_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_per_replica_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistribution_strategy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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 309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py\u001b[0m in \u001b[0;36mvalidate_per_replica_inputs\u001b[0;34m(distribution_strategy, x)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\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 355\u001b[0m \u001b[0;31m# Validate that the shape and dtype of all the elements in x are the same.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 356\u001b[0;31m \u001b[0mvalidate_all_tensor_shapes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_values\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 357\u001b[0m \u001b[0mvalidate_all_tensor_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py\u001b[0m in \u001b[0;36mvalidate_all_tensor_shapes\u001b[0;34m(x, x_values)\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvalidate_all_tensor_shapes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_values\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 372\u001b[0m \u001b[0;31m# Validate that the shape of all the elements in x have the same shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m \u001b[0mx_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_list\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 374\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_values\u001b[0m\u001b[0;34m)\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 375\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mx_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow_core/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mas_list\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1169\u001b[0m \"\"\"\n\u001b[1;32m 1170\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dims\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1171\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"as_list() is not defined on an unknown TensorShape.\"\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 1172\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dims\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: as_list() is not defined on an unknown TensorShape."
]
}
],
"source": [
"orig = tf.data.Dataset.from_generator(\n",
" generator=gen, \n",
" output_types=(tf.float32, tf.float32),\n",
" output_shapes=([None, INPUT_SHAPE[1]], ()),\n",
" args=([NUM_POINTS, INPUT_SHAPE])\n",
").batch(BATCH_SIZE)\n",
"\n",
"read_ds = save_n_load(orig)\n",
"\n",
"model = create_model(input_shape=INPUT_SHAPE)\n",
"model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4, clipvalue=1.0),\n",
" loss= tf.keras.losses.CategoricalCrossentropy(),\n",
")\n",
"print(model.summary())\n",
"hist = model.fit(read_ds, epochs=EPOCHS, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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