How to build an unsupervised CNN model with keras/tensorflow?

Question

I'm trying to build a CNN for an image-to-image translation application, the input of the model is an image, and the output is a confidence map. There are no labeled confidence as the ground truth during training, but a loss function is designed to guide the model to a proper output.

I'm having trouble building the neural network with keras, because the model.fit() function needs both the training data and the labeled data (ground truth data).

So my question is ,is there a way to build an unsupervised CNN with keras or tensorflow? If so, what should i do to build one? Is there an application example or something i can refer to?

Thanks in advance!

Answer 1

You can build an unsupervised CNN with keras using Auto Encoders. The code for it, for Fashion MNIST Data, is shown below:

# Python ?3.5 is required
import sys
assert sys.version_info >= (3, 5)

# Scikit-Learn ?0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"

# TensorFlow ?2.0-preview is required
import tensorflow as tf
from tensorflow import keras
assert tf.__version__ >= "2.0"

# Common imports
import numpy as np
import os

(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()
X_train_full = X_train_full.astype(np.float32) / 255
X_test = X_test.astype(np.float32) / 255
X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]
y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]

def rounded_accuracy(y_true, y_pred):
    return keras.metrics.binary_accuracy(tf.round(y_true), tf.round(y_pred))

tf.random.set_seed(42)
np.random.seed(42)

conv_encoder = keras.models.Sequential([
    keras.layers.Reshape([28, 28, 1], input_shape=[28, 28]),
    keras.layers.Conv2D(16, kernel_size=3, padding="SAME", activation="selu"),
    keras.layers.MaxPool2D(pool_size=2),
    keras.layers.Conv2D(32, kernel_size=3, padding="SAME", activation="selu"),
    keras.layers.MaxPool2D(pool_size=2),
    keras.layers.Conv2D(64, kernel_size=3, padding="SAME", activation="selu"),
    keras.layers.MaxPool2D(pool_size=2)
])
conv_decoder = keras.models.Sequential([
    keras.layers.Conv2DTranspose(32, kernel_size=3, strides=2, padding="VALID", activation="selu",
                                 input_shape=[3, 3, 64]),
    keras.layers.Conv2DTranspose(16, kernel_size=3, strides=2, padding="SAME", activation="selu"),
    keras.layers.Conv2DTranspose(1, kernel_size=3, strides=2, padding="SAME", activation="sigmoid"),
    keras.layers.Reshape([28, 28])
])
conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])

conv_ae.compile(loss="binary_crossentropy", optimizer=keras.optimizers.SGD(lr=1.0),
                metrics=[rounded_accuracy])
history = conv_ae.fit(X_train, X_train, epochs=5,
                      validation_data=[X_valid, X_valid])

conv_encoder.summary()
conv_decoder.summary()

You can refer to this link for more information.