Update Readme.md

Author: purvasingh96

Chapter-wise code/Code - PyTorch/4. Generative Adversarial Networks (GANs)/4. Generate Faces via DCGAN/Readme.md

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+# Generating fake faces using GANs
+
+## Algorithm
+In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate new images of faces that look as realistic as possible!<br>
+
+1. Get the celeb faces data, pre-process it and load the data.
+2. Define the model :  Discriminator and Generator
+    * **Discriminator**
+      * The inputs to the discriminator are 32x32x3 tensor images
+      * 3 convolutional layers, 1 fully-connected layer and leaky_relu activation function.
+      * The output should be a single value that will indicate whether a given image is real or fake
+    * **Generator**
+      * The inputs to the generator are vectors of some length z_size
+      * 3 transpose_convolutional_layer, 1 fully-connected layer and 1 relu activation function
+      * The output should be a image of shape 32x32x3
+3. Generate discriminator and generator losses
+    * **Discriminator Loss**
+        * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss.`
+        * Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.
+    * **Generator Loss**
+        * The generator loss will look similar only with flipped labels.
+4. Train the network
+5. Plot the loss
+6. Generator samples from training
+
+
+
+## Results
+
+1. Final discriminator and generator loss are as follows - <br>
+    * Discriminator loss : 0.4530
+    * Generator loss     : 3.5706
+    
+2. Training loss of discrimiator and generator plotted over each epoch - <br>
+<img src="./images/generator_discriminator_loss.png"></img><br><br>
+
+3. Fake face images generated after training the GAN -<br>
+<img src="./images/generated_faces.png"></img><br><br>