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Reenable files when TensorFlow supports the current Python (#8602)
* Remove python_version < "3.11" for tensorflow * Reenable neural_network/input_data.py_tf * updating DIRECTORY.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Try to fix ruff * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Try to fix ruff * Try to fix ruff * Try to fix ruff * Try to fix pre-commit * Try to fix * Fix * Fix * Reenable dynamic_programming/k_means_clustering_tensorflow.py_tf * updating DIRECTORY.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Try to fix ruff --------- Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Maxim Smolskiy
GitHub
parent
84b6852de8
commit
56a40eb3ee
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@ -309,6 +309,7 @@
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* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
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* [Integer Partition](dynamic_programming/integer_partition.py)
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* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
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* [K Means Clustering Tensorflow](dynamic_programming/k_means_clustering_tensorflow.py)
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* [Knapsack](dynamic_programming/knapsack.py)
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* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
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* [Longest Common Substring](dynamic_programming/longest_common_substring.py)
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@ -685,6 +686,7 @@
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* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
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* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
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* [Convolution Neural Network](neural_network/convolution_neural_network.py)
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* [Input Data](neural_network/input_data.py)
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* [Perceptron](neural_network/perceptron.py)
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* [Simple Neural Network](neural_network/simple_neural_network.py)
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@ -1,9 +1,10 @@
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import tensorflow as tf
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from random import shuffle
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import tensorflow as tf
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from numpy import array
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def TFKMeansCluster(vectors, noofclusters):
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def tf_k_means_cluster(vectors, noofclusters):
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"""
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K-Means Clustering using TensorFlow.
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'vectors' should be a n*k 2-D NumPy array, where n is the number
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@ -30,7 +31,6 @@ def TFKMeansCluster(vectors, noofclusters):
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graph = tf.Graph()
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with graph.as_default():
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# SESSION OF COMPUTATION
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sess = tf.Session()
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@ -95,8 +95,7 @@ def TFKMeansCluster(vectors, noofclusters):
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# iterations. To keep things simple, we will only do a set number of
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# iterations, instead of using a Stopping Criterion.
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noofiterations = 100
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for iteration_n in range(noofiterations):
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for _ in range(noofiterations):
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##EXPECTATION STEP
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##Based on the centroid locations till last iteration, compute
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##the _expected_ centroid assignments.
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@ -21,13 +21,10 @@ This module and all its submodules are deprecated.
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import collections
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import gzip
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import os
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import urllib
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import numpy
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from six.moves import urllib
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from six.moves import xrange # pylint: disable=redefined-builtin
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import random_seed
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from tensorflow.python.framework import dtypes, random_seed
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from tensorflow.python.platform import gfile
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from tensorflow.python.util.deprecation import deprecated
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@ -46,16 +43,16 @@ def _read32(bytestream):
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def _extract_images(f):
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"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
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Args:
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f: A file object that can be passed into a gzip reader.
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Args:
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f: A file object that can be passed into a gzip reader.
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Returns:
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data: A 4D uint8 numpy array [index, y, x, depth].
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Returns:
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data: A 4D uint8 numpy array [index, y, x, depth].
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Raises:
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ValueError: If the bytestream does not start with 2051.
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Raises:
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ValueError: If the bytestream does not start with 2051.
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"""
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"""
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print("Extracting", f.name)
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with gzip.GzipFile(fileobj=f) as bytestream:
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magic = _read32(bytestream)
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@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
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def _extract_labels(f, one_hot=False, num_classes=10):
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"""Extract the labels into a 1D uint8 numpy array [index].
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Args:
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f: A file object that can be passed into a gzip reader.
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one_hot: Does one hot encoding for the result.
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num_classes: Number of classes for the one hot encoding.
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Args:
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f: A file object that can be passed into a gzip reader.
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one_hot: Does one hot encoding for the result.
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num_classes: Number of classes for the one hot encoding.
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Returns:
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labels: a 1D uint8 numpy array.
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Returns:
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labels: a 1D uint8 numpy array.
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Raises:
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ValueError: If the bystream doesn't start with 2049.
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"""
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Raises:
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ValueError: If the bystream doesn't start with 2049.
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"""
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print("Extracting", f.name)
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with gzip.GzipFile(fileobj=f) as bytestream:
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magic = _read32(bytestream)
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@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
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class _DataSet:
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"""Container class for a _DataSet (deprecated).
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THIS CLASS IS DEPRECATED.
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"""
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THIS CLASS IS DEPRECATED.
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"""
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@deprecated(
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None,
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@ -135,21 +132,21 @@ class _DataSet:
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):
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"""Construct a _DataSet.
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one_hot arg is used only if fake_data is true. `dtype` can be either
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`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
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`[0, 1]`. Seed arg provides for convenient deterministic testing.
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one_hot arg is used only if fake_data is true. `dtype` can be either
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`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
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`[0, 1]`. Seed arg provides for convenient deterministic testing.
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Args:
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images: The images
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labels: The labels
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fake_data: Ignore inages and labels, use fake data.
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one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
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False).
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dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
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range [0,255]. float32 output has range [0,1].
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reshape: Bool. If True returned images are returned flattened to vectors.
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seed: The random seed to use.
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"""
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Args:
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images: The images
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labels: The labels
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fake_data: Ignore inages and labels, use fake data.
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one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
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False).
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dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
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range [0,255]. float32 output has range [0,1].
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reshape: Bool. If True returned images are returned flattened to vectors.
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seed: The random seed to use.
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"""
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seed1, seed2 = random_seed.get_seed(seed)
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# If op level seed is not set, use whatever graph level seed is returned
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numpy.random.seed(seed1 if seed is None else seed2)
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@ -206,8 +203,8 @@ class _DataSet:
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else:
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fake_label = 0
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return (
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[fake_image for _ in xrange(batch_size)],
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[fake_label for _ in xrange(batch_size)],
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[fake_image for _ in range(batch_size)],
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[fake_label for _ in range(batch_size)],
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)
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start = self._index_in_epoch
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# Shuffle for the first epoch
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@ -250,19 +247,19 @@ class _DataSet:
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def _maybe_download(filename, work_directory, source_url):
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"""Download the data from source url, unless it's already here.
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Args:
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filename: string, name of the file in the directory.
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work_directory: string, path to working directory.
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source_url: url to download from if file doesn't exist.
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Args:
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filename: string, name of the file in the directory.
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work_directory: string, path to working directory.
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source_url: url to download from if file doesn't exist.
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Returns:
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Path to resulting file.
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"""
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Returns:
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Path to resulting file.
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"""
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if not gfile.Exists(work_directory):
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gfile.MakeDirs(work_directory)
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filepath = os.path.join(work_directory, filename)
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if not gfile.Exists(filepath):
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urllib.request.urlretrieve(source_url, filepath)
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urllib.request.urlretrieve(source_url, filepath) # noqa: S310
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with gfile.GFile(filepath) as f:
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size = f.size()
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print("Successfully downloaded", filename, size, "bytes.")
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@ -328,7 +325,8 @@ def read_data_sets(
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if not 0 <= validation_size <= len(train_images):
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raise ValueError(
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f"Validation size should be between 0 and {len(train_images)}. Received: {validation_size}."
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f"Validation size should be between 0 and {len(train_images)}. "
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f"Received: {validation_size}."
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)
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validation_images = train_images[:validation_size]
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@ -336,7 +334,7 @@ def read_data_sets(
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train_images = train_images[validation_size:]
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train_labels = train_labels[validation_size:]
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options = dict(dtype=dtype, reshape=reshape, seed=seed)
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options = {"dtype": dtype, "reshape": reshape, "seed": seed}
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train = _DataSet(train_images, train_labels, **options)
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validation = _DataSet(validation_images, validation_labels, **options)
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@ -15,7 +15,7 @@ scikit-fuzzy
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scikit-learn
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statsmodels
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sympy
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tensorflow; python_version < "3.11"
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tensorflow
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texttable
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tweepy
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xgboost
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