Code clean up and README work

PanoramAI/VAEorama.py

@@ -23,36 +23,34 @@ def __init__(self, dataset,
                  latent_dim = 100):
         super().__init__(dataset, BATCH_SIZE, test_size, latent_dim)
 
-    def _generate_random_vector(self, n_samples):
-        self.n_samples_to_generate = n_samples
-        return tf.random.normal(
-            shape=[n_samples, self.latent_dim])
-
     def generate_samples(self, n_samples):
-        #if n_samples > self.n_samples_to_generate:
-        #    print("Regenerating sample vector.")
-        return self.model.sample(self._generate_random_vector(n_samples))
-
+        self.n_samples_to_generate = n_samples
+        return self.model.sample(
+            tf.random.normal(shape=[n_samples, self.latent_dim]))
+        
     def create_model(self):
         M, N = self.dimensions
         self.model = _CVAE(M, N, self.latent_dim)
         return
 
-    def log_normal_pdf(self, sample, mean, logvar, raxis=1):
-        log2pi = tf.math.log(2. * np.pi)
-        return tf.reduce_sum(
-            -.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi),
-            axis=raxis)
-
     @tf.function
     def compute_loss(self, x):
         mean, logvar = self.model.encode(x)
         z = self.model.reparameterize(mean, logvar)
         x_predicted = self.model.decode(z)
         MSE = tf.losses.MSE(x, x_predicted)
         logpx_z = -tf.reduce_sum(MSE)
-        logpz = self.log_normal_pdf(z, 0., 0.)
-        logqz_x = self.log_normal_pdf(z, mean, logvar)
+        #Log-normal distributions for z
+        #The prior has mean 0 with no variance
+        #The variational distribution has a mean and logvar
+        log2pi = tf.math.log(2. * np.pi)
+        logpz = tf.reduce_sum(
+            -0.5 * (z ** 2. + log2pi), axis=1)
+        logqz_x = tf.reduce_sum(
+            -.5 * ((z - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi),
+            axis=1)
+        #logpz = self.log_normal_pdf(z, 0., 0.)
+        #logqz_x = self.log_normal_pdf(z, mean, logvar)
         return -tf.reduce_mean(logpx_z + logpz - logqz_x)
 
     @tf.function

README.md

@@ -26,4 +26,30 @@ These can all be installed together using the `requirements.txt` file as shown a
 
 ## Usage
 
-This section is still being finalized.
+PanoramAI contains models to generate panoramic images. Models provided in this package are untrained. This section documents how to instantiate and train a model, and then how to generate new panoramic images.
+
+First, import and create a model. As an example here, we consider a convolutional variational autoencoder (`VAEorama`).
+
+```python
+import PanoramAI
+
+model = PanoramAI.VAEorama(dataset)
+```
+
+In this example, `dataset` is a `numpy.ndarray` containing the input panoramic images for training. It should have `N` RGB images in total, meaning its shape must be (`N`,`Height`,`Width`,`3`) where the height and width are in pixels.
+
+Once created, we train the model for some number of epochs.
+```python
+epochs = 100
+model.train(epochs)
+```
+
+Predictions can be made in batches
+```python
+#Create 10 sample panoramas
+samples - model.generate_samples(10)
+```
+
+### Models
+
+At present, PanoramAI includes two fully generative models: a DCGAN and a convolutional variational autoencoder (VAE). A conditional VAE is in development.