Datasets:
HenryAI
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KerasCodeExamples.txt

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4.99k
199/199 [==============================] - 31s 157ms/step - loss: 0.2360 - val_loss: 0.3950
Epoch 11/15
199/199 [==============================] - 31s 157ms/step - loss: 0.2247 - val_loss: 0.4139
Epoch 12/15
199/199 [==============================] - 31s 157ms/step - loss: 0.2126 - val_loss: 0.3861
Epoch 13/15
199/199 [==============================] - 31s 157ms/step - loss: 0.2026 - val_loss: 0.4138
Epoch 14/15
199/199 [==============================] - 31s 156ms/step - loss: 0.1932 - val_loss: 0.4265
Epoch 15/15
199/199 [==============================] - 31s 157ms/step - loss: 0.1857 - val_loss: 0.3959
<tensorflow.python.keras.callbacks.History at 0x7f6e11107b70>
Visualize predictions
# Generate predictions for all images in the validation set
val_gen = OxfordPets(batch_size, img_size, val_input_img_paths, val_target_img_paths)
val_preds = model.predict(val_gen)
def display_mask(i):
\"\"\"Quick utility to display a model's prediction.\"\"\"
mask = np.argmax(val_preds[i], axis=-1)
mask = np.expand_dims(mask, axis=-1)
img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))
display(img)
# Display results for validation image #10
i = 10
# Display input image
display(Image(filename=val_input_img_paths[i]))
# Display ground-truth target mask
img = PIL.ImageOps.autocontrast(load_img(val_target_img_paths[i]))
display(img)
# Display mask predicted by our model
display_mask(i) # Note that the model only sees inputs at 150x150.
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Similarity learning using a siamese network trained with a contrastive loss.
Introduction
Siamese Networks are neural networks which share weights between two or more sister networks, each producing embedding vectors of its respective inputs.
In supervised similarity learning, the networks are then trained to maximize the contrast (distance) between embeddings of inputs of different classes, while minimizing the distance between embeddings of similar classes, resulting in embedding spaces that reflect the class segmentation of the training inputs.
Setup
import random
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
Hyperparameters
epochs = 10
batch_size = 16
margin = 1 # Margin for constrastive loss.
Load the MNIST dataset
(x_train_val, y_train_val), (x_test, y_test) = keras.datasets.mnist.load_data()
# Change the data type to a floating point format
x_train_val = x_train_val.astype(\"float32\")
x_test = x_test.astype(\"float32\")
Define training and validation sets
# Keep 50% of train_val in validation set
x_train, x_val = x_train_val[:30000], x_train_val[30000:]
y_train, y_val = y_train_val[:30000], y_train_val[30000:]
del x_train_val, y_train_val
Create pairs of images
We will train the model to differentiate between digits of different classes. For example, digit 0 needs to be differentiated from the rest of the digits (1 through 9), digit 1 - from 0 and 2 through 9, and so on. To carry this out, we will select N random images from class A (for example, for digit 0) and pair them wi...
def make_pairs(x, y):
\"\"\"Creates a tuple containing image pairs with corresponding label.
Arguments:
x: List containing images, each index in this list corresponds to one image.
y: List containing labels, each label with datatype of `int`.
Returns:
Tuple containing two numpy arrays as (pairs_of_samples, labels),
where pairs_of_samples' shape is (2len(x), 2,n_features_dims) and
labels are a binary array of shape (2len(x)).
\"\"\"
num_classes = max(y) + 1
digit_indices = [np.where(y == i)[0] for i in range(num_classes)]
pairs = []
labels = []
for idx1 in range(len(x)):
# add a matching example
x1 = x[idx1]