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HenryAI
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KerasCodeExamples.txt

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train_size = int(len(caption_data) * train_size)
training_data = {
img_name: caption_data[img_name] for img_name in all_images[:train_size]
}
validation_data = {
img_name: caption_data[img_name] for img_name in all_images[train_size:]
}
# 4. Return the splits
return training_data, validation_data
# Load the dataset
captions_mapping, text_data = load_captions_data(\"Flickr8k.token.txt\")
# Split the dataset into training and validation sets
train_data, valid_data = train_val_split(captions_mapping)
print(\"Number of training samples: \", len(train_data))
print(\"Number of validation samples: \", len(valid_data))
Number of training samples: 6114
Number of validation samples: 1529
Number of training samples: 6114
Number of validation samples: 1529
Vectorizing the text data
We'll use the TextVectorization layer to vectorize the text data, that is to say, to turn the original strings into integer sequences where each integer represents the index of a word in a vocabulary. We will use a custom string standardization scheme (strip punctuation characters except < and >) and the default splitt...
def custom_standardization(input_string):
lowercase = tf.strings.lower(input_string)
return tf.strings.regex_replace(lowercase, \"[%s]\" % re.escape(strip_chars), \"\")
# [KERASBERT PROCESSING] removed definition of special chars for import
strip_chars = strip_chars.replace(\"<\", \"\")
strip_chars = strip_chars.replace(\">\", \"\")
vectorization = TextVectorization(
max_tokens=VOCAB_SIZE,
output_mode=\"int\",
output_sequence_length=SEQ_LENGTH,
standardize=custom_standardization,
)
vectorization.adapt(text_data)
# Data augmentation for image data
image_augmentation = keras.Sequential(
[
layers.RandomFlip(\"horizontal\"),
layers.RandomRotation(0.2),
layers.RandomContrast(0.3),
]
)
2021-09-17 05:17:57.047819: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.058177: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.106007: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.107650: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-09-17 05:17:57.134387: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.135154: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.135806: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.680010: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.680785: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.681439: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-17 05:17:57.682067: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14684 MB memory: -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:04.0, compute capability: 7.0
2021-09-17 05:17:58.229404: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Building a tf.data.Dataset pipeline for training
We will generate pairs of images and corresponding captions using a tf.data.Dataset object. The pipeline consists of two steps:
Read the image from the disk
Tokenize all the five captions corresponding to the image
def decode_and_resize(img_path):
img = tf.io.read_file(img_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize(img, IMAGE_SIZE)
img = tf.image.convert_image_dtype(img, tf.float32)
return img
def process_input(img_path, captions):
return decode_and_resize(img_path), vectorization(captions)
def make_dataset(images, captions):
if split == \"train\":
img_dataset = tf.data.Dataset.from_tensor_slices(images).map(
read_train_image, num_parallel_calls=AUTOTUNE
)
else:
img_dataset = tf.data.Dataset.from_tensor_slices(images).map(
read_valid_image, num_parallel_calls=AUTOTUNE
)
cap_dataset = tf.data.Dataset.from_tensor_slices(captions).map(
vectorization, num_parallel_calls=AUTOTUNE
)
dataset = tf.data.Dataset.zip((img_dataset, cap_dataset))
dataset = dataset.batch(BATCH_SIZE).shuffle(256).prefetch(AUTOTUNE)
return dataset