mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
synced 2026-02-21 11:18:01 +00:00
136 lines
3.6 KiB
Python
136 lines
3.6 KiB
Python
import os
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import matplotlib.pyplot
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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import tensorflow_datasets as tfds
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physical_devices = tf.config.list_physical_devices("GPU")
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tf.config.experimental.set_memory_growth(physical_devices[0], True)
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(ds_train, ds_test), ds_info = tfds.load(
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"mnist",
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split=["train", "test"],
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shuffle_files=True,
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as_supervised=True, # will return tuple (img, label) otherwise dict
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with_info=True, # able to get info about dataset
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)
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# fig = tfds.show_examples(ds_train, ds_info, rows=4, cols=4)
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# print(ds_info)
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def normalize_img(image, label):
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"""Normalizes images"""
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return tf.cast(image, tf.float32) / 255.0, label
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AUTOTUNE = tf.data.experimental.AUTOTUNE
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BATCH_SIZE = 128
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# Setup for train dataset
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ds_train = ds_train.map(normalize_img, num_parallel_calls=AUTOTUNE)
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ds_train = ds_train.cache()
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ds_train = ds_train.shuffle(ds_info.splits["train"].num_examples)
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ds_train = ds_train.batch(BATCH_SIZE)
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ds_train = ds_train.prefetch(AUTOTUNE)
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# Setup for test Dataset
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ds_test = ds_train.map(normalize_img, num_parallel_calls=AUTOTUNE)
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ds_test = ds_train.batch(128)
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ds_test = ds_train.prefetch(AUTOTUNE)
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model = keras.Sequential(
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[
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keras.Input((28, 28, 1)),
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layers.Conv2D(32, 3, activation="relu"),
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layers.Flatten(),
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tf.keras.layers.Dense(10, activation="softmax"),
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]
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)
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model.compile(
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optimizer=keras.optimizers.Adam(0.001),
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loss=keras.losses.SparseCategoricalCrossentropy(),
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metrics=["accuracy"],
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)
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model.fit(ds_train, epochs=5, verbose=2)
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model.evaluate(ds_test)
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(ds_train, ds_test), ds_info = tfds.load(
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"imdb_reviews",
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split=["train", "test"],
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shuffle_files=True,
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as_supervised=True, # will return tuple (img, label) otherwise dict
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with_info=True, # able to get info about dataset
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)
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tokenizer = tfds.features.text.Tokenizer()
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def build_vocabulary():
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vocabulary = set()
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for text, _ in ds_train:
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vocabulary.update(tokenizer.tokenize(text.numpy().lower()))
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return vocabulary
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vocabulary = build_vocabulary()
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encoder = tfds.features.text.TokenTextEncoder(
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list(vocabulary), oov_token="<UNK>", lowercase=True, tokenizer=tokenizer
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)
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def my_enc(text_tensor, label):
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encoded_text = encoder.encode(text_tensor.numpy())
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return encoded_text, label
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def encode_map_fn(text, label):
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# py_func doesn't set the shape of the returned tensors.
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encoded_text, label = tf.py_function(
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my_enc, inp=[text, label], Tout=(tf.int64, tf.int64)
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)
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# `tf.data.Datasets` work best if all components have a shape set
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# so set the shapes manually:
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encoded_text.set_shape([None])
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label.set_shape([])
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return encoded_text, label
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AUTOTUNE = tf.data.experimental.AUTOTUNE
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ds_train = ds_train.map(encode_map_fn, num_parallel_calls=AUTOTUNE)
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ds_train = ds_train.cache()
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ds_train = ds_train.shuffle(1000)
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ds_train = ds_train.padded_batch(32, padded_shapes=([None], ()))
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ds_train = ds_train.prefetch(AUTOTUNE)
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ds_test = ds_test.map(encode_map_fn)
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ds_test = ds_test.padded_batch(32, padded_shapes=([None], ()))
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model = keras.Sequential(
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[
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layers.Masking(mask_value=0),
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layers.Embedding(input_dim=len(vocabulary) + 2, output_dim=32),
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layers.GlobalAveragePooling1D(),
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layers.Dense(64, activation="relu"),
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layers.Dense(1),
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]
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)
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model.compile(
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loss=keras.losses.BinaryCrossentropy(from_logits=True),
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optimizer=keras.optimizers.Adam(3e-4, clipnorm=1),
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metrics=["accuracy"],
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)
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model.fit(ds_train, epochs=15, verbose=2)
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model.evaluate(ds_test)
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