HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

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Epoch 34/100
53/53 [==============================] - 61s 1s/step - loss: 0.0533 - accuracy: 0.9828 - val_loss: 0.0161 - val_accuracy: 0.9947
Epoch 35/100
53/53 [==============================] - 61s 1s/step - loss: 0.0258 - accuracy: 0.9911 - val_loss: 0.0277 - val_accuracy: 0.9867
Epoch 36/100
53/53 [==============================] - 60s 1s/step - loss: 0.0261 - accuracy: 0.9901 - val_loss: 0.0542 - val_accuracy: 0.9787
Epoch 37/100
53/53 [==============================] - 60s 1s/step - loss: 0.0368 - accuracy: 0.9877 - val_loss: 0.0699 - val_accuracy: 0.9813
Epoch 38/100
53/53 [==============================] - 63s 1s/step - loss: 0.0251 - accuracy: 0.9890 - val_loss: 0.0206 - val_accuracy: 0.9907
Epoch 39/100
53/53 [==============================] - 62s 1s/step - loss: 0.0220 - accuracy: 0.9913 - val_loss: 0.0211 - val_accuracy: 0.9947
Evaluation
print(model.evaluate(valid_ds))
24/24 [==============================] - 6s 244ms/step - loss: 0.0146 - accuracy: 0.9947
[0.014629718847572803, 0.9946666955947876]
We get ~ 98% validation accuracy.

Demonstration
Let's take some samples and:

Predict the speaker
Compare the prediction with the real speaker
Listen to the audio to see that despite the samples being noisy, the model is still pretty accurate
SAMPLES_TO_DISPLAY = 10

test_ds = paths_and_labels_to_dataset(valid_audio_paths, valid_labels)
test_ds = test_ds.shuffle(buffer_size=BATCH_SIZE * 8, seed=SHUFFLE_SEED).batch(
BATCH_SIZE
)

test_ds = test_ds.map(lambda x, y: (add_noise(x, noises, scale=SCALE), y))

for audios, labels in test_ds.take(1):
# Get the signal FFT
ffts = audio_to_fft(audios)
# Predict
y_pred = model.predict(ffts)
# Take random samples
rnd = np.random.randint(0, BATCH_SIZE, SAMPLES_TO_DISPLAY)
audios = audios.numpy()[rnd, :, :]
labels = labels.numpy()[rnd]
y_pred = np.argmax(y_pred, axis=-1)[rnd]

for index in range(SAMPLES_TO_DISPLAY):
# For every sample, print the true and predicted label
# as well as run the voice with the noise
print(
\"Speaker: {} - Predicted: {}\".format(
class_names[labels[index]],
class_names[y_pred[index]],
)
)
display(Audio(audios[index, :, :].squeeze(), rate=SAMPLING_RATE))

Train a 3D convolutional neural network to predict presence of pneumonia.

Introduction
This example will show the steps needed to build a 3D convolutional neural network (CNN) to predict the presence of viral pneumonia in computer tomography (CT) scans. 2D CNNs are commonly used to process RGB images (3 channels). A 3D CNN is simply the 3D equivalent: it takes as input a 3D volume or a sequence of 2D frames (e.g. slices in a CT scan), 3D CNNs are a powerful model for learning representations for volumetric data.

References
A survey on Deep Learning Advances on Different 3D DataRepresentations
VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition
FusionNet: 3D Object Classification Using MultipleData Representations
Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
Setup
import os
import zipfile
import numpy as np
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
Downloading the MosMedData: Chest CT Scans with COVID-19 Related Findings
In this example, we use a subset of the MosMedData: Chest CT Scans with COVID-19 Related Findings. This dataset consists of lung CT scans with COVID-19 related findings, as well as without such findings.

We will be using the associated radiological findings of the CT scans as labels to build a classifier to predict presence of viral pneumonia. Hence, the task is a binary classification problem.

# Download url of normal CT scans.
url = \"https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-0.zip\"
filename = os.path.join(os.getcwd(), \"CT-0.zip\")
keras.utils.get_file(filename, url)

# Download url of abnormal CT scans.
url = \"https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-23.zip\"
filename = os.path.join(os.getcwd(), \"CT-23.zip\")
keras.utils.get_file(filename, url)

# Make a directory to store the data.
os.makedirs(\"MosMedData\")

# Unzip data in the newly created directory.
with zipfile.ZipFile(\"CT-0.zip\", \"r\") as z_fp:
z_fp.extractall(\"./MosMedData/\")

with zipfile.ZipFile(\"CT-23.zip\", \"r\") as z_fp:
z_fp.extractall(\"./MosMedData/\")
Downloading data from https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-0.zip
1065476096/1065471431 [==============================] - 236s 0us/step
Downloading data from https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-23.zip

Epoch 34/100

53/53 [==============================] - 61s 1s/step - loss: 0.0533 - accuracy: 0.9828 - val_loss: 0.0161 - val_accuracy: 0.9947

Epoch 35/100

53/53 [==============================] - 61s 1s/step - loss: 0.0258 - accuracy: 0.9911 - val_loss: 0.0277 - val_accuracy: 0.9867

Epoch 36/100

53/53 [==============================] - 60s 1s/step - loss: 0.0261 - accuracy: 0.9901 - val_loss: 0.0542 - val_accuracy: 0.9787

Epoch 37/100

53/53 [==============================] - 60s 1s/step - loss: 0.0368 - accuracy: 0.9877 - val_loss: 0.0699 - val_accuracy: 0.9813

Epoch 38/100

53/53 [==============================] - 63s 1s/step - loss: 0.0251 - accuracy: 0.9890 - val_loss: 0.0206 - val_accuracy: 0.9907

Epoch 39/100

53/53 [==============================] - 62s 1s/step - loss: 0.0220 - accuracy: 0.9913 - val_loss: 0.0211 - val_accuracy: 0.9947

Evaluation

print(model.evaluate(valid_ds))

24/24 [==============================] - 6s 244ms/step - loss: 0.0146 - accuracy: 0.9947

[0.014629718847572803, 0.9946666955947876]

We get ~ 98% validation accuracy.

Demonstration

Let's take some samples and:

Predict the speaker

Compare the prediction with the real speaker

Listen to the audio to see that despite the samples being noisy, the model is still pretty accurate

SAMPLES_TO_DISPLAY = 10

test_ds = paths_and_labels_to_dataset(valid_audio_paths, valid_labels)

test_ds = test_ds.shuffle(buffer_size=BATCH_SIZE * 8, seed=SHUFFLE_SEED).batch(

BATCH_SIZE

)

test_ds = test_ds.map(lambda x, y: (add_noise(x, noises, scale=SCALE), y))

for audios, labels in test_ds.take(1):

# Get the signal FFT

ffts = audio_to_fft(audios)

# Predict

y_pred = model.predict(ffts)

# Take random samples

rnd = np.random.randint(0, BATCH_SIZE, SAMPLES_TO_DISPLAY)

audios = audios.numpy()[rnd, :, :]

labels = labels.numpy()[rnd]

y_pred = np.argmax(y_pred, axis=-1)[rnd]

for index in range(SAMPLES_TO_DISPLAY):

# For every sample, print the true and predicted label

# as well as run the voice with the noise

print(

\"Speaker: {} - Predicted: {}\".format(

class_names[labels[index]],

class_names[y_pred[index]],

)

display(Audio(audios[index, :, :].squeeze(), rate=SAMPLING_RATE))

Train a 3D convolutional neural network to predict presence of pneumonia.

Introduction

This example will show the steps needed to build a 3D convolutional neural network (CNN) to predict the presence of viral pneumonia in computer tomography (CT) scans. 2D CNNs are commonly used to process RGB images (3 channels). A 3D CNN is simply the 3D equivalent: it takes as input a 3D volume or a sequence of 2D frames (e.g. slices in a CT scan), 3D CNNs are a powerful model for learning representations for volumetric data.

References

A survey on Deep Learning Advances on Different 3D DataRepresentations

VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition

FusionNet: 3D Object Classification Using MultipleData Representations

Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction

Setup

import os

import zipfile

import numpy as np

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

Downloading the MosMedData: Chest CT Scans with COVID-19 Related Findings

In this example, we use a subset of the MosMedData: Chest CT Scans with COVID-19 Related Findings. This dataset consists of lung CT scans with COVID-19 related findings, as well as without such findings.

We will be using the associated radiological findings of the CT scans as labels to build a classifier to predict presence of viral pneumonia. Hence, the task is a binary classification problem.

# Download url of normal CT scans.

url = \"https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-0.zip\"

filename = os.path.join(os.getcwd(), \"CT-0.zip\")

keras.utils.get_file(filename, url)

# Download url of abnormal CT scans.

url = \"https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-23.zip\"

filename = os.path.join(os.getcwd(), \"CT-23.zip\")

keras.utils.get_file(filename, url)

# Make a directory to store the data.

os.makedirs(\"MosMedData\")

# Unzip data in the newly created directory.

with zipfile.ZipFile(\"CT-0.zip\", \"r\") as z_fp:

z_fp.extractall(\"./MosMedData/\")

with zipfile.ZipFile(\"CT-23.zip\", \"r\") as z_fp:

z_fp.extractall(\"./MosMedData/\")

Downloading data from https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-0.zip

1065476096/1065471431 [==============================] - 236s 0us/step

Downloading data from https://github.com/hasibzunair/3D-image-classification-tutorial/releases/download/v0.2/CT-23.zip