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merishnaSuwal / Linear_reg.ipynb
Last active Sep 21, 2018
Linear regression for predicting rainfall
View Linear_reg.ipynb
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@merishnaSuwal
merishnaSuwal / breast_cancer_prediction.ipynb
Last active Sep 24, 2018
Simple Breast cancer prediction using Logistic Regression
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View GAN_discriminator.py
def discriminator(x, is_reuse=False, alpha = 0.2):
''' Build the discriminator network.
Arguments
---------
x : Input tensor for the discriminator
n_units: Number of units in hidden layer
reuse : Reuse the variables with tf.variable_scope
alpha : leak parameter for leaky ReLU
View GAN_generator.py
def generator(z, output_channel_dim, is_train=True):
''' Building the generator network.
Arguments
---------
z : Input tensor for the generator
output_channel_dim : Shape of the generator output
n_units : Number of units in hidden layer
reuse : Reuse the variables with tf.variable_scope
alpha : leak parameter for leaky ReLU
View gan_model_inputs.py
def gan_model_inputs(real_dim, z_dim):
"""
Creates the inputs for the model.
Arguments:
----------
:param real_dim: tuple containing width, height and channels
:param z_dim: The dimension of Z
----------
Returns:
View image_resize.py
# Define the directory with real image data
data_dir = './data/' # Data
resized_data_dir = "./resized_data" # folder for saving resized data
# Resize images into 128x128
preprocess = True # set to False if no resizing
if preprocess == True:
# Create resized folder if not exist
if not os.path.exists(resized_data_dir):
View losses.py
def gan_model_loss(input_real, input_z, output_channel_dim, alpha):
"""
Get the loss for the discriminator and generator
Arguments:
---------
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
---------
View optimizers.py
def gan_model_optimizers(d_loss, g_loss, disc_lr, gen_lr, beta1):
"""
Get optimization operations
Arguments:
----------
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param disc_lr: Placeholder for Learning Rate for discriminator
:param gen_lr: Placeholder for Learning Rate for generator
View generator_output.py
def generator_output(sess, n_images, input_z, output_channel_dim, image_mode, image_path):
"""
Save output from the generator.
Arguments:
----------
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor (noise vector)
:param output_channel_dim: The number of channels in the output image
View train_gan_model.py
def train_gan_model(epoch, batch_size, z_dim, learning_rate_D, learning_rate_G, beta1, get_batches, data_shape, data_image_mode, alpha):
"""
Train the GAN model.
Arguments:
----------
:param epoch: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate