merishnaSuwal

# Selecting columns with data type as 'object'
columns = dataset.columns[dataset.dtypes.eq('object')]

# Convert to numeric values
dataset[columns] = dataset[columns].apply(pd.to_numeric, errors='coerce')

# Viewing the details
dataset.info()

merishnaSuwal

# Dropping the TBG column as it contains extremely high number of null values
dataset.drop('TBG', axis = 1, inplace=True)

merishnaSuwal

# Replacing ? into NaN values 
dataset.replace(to_replace='?', inplace=True, value=np.NaN)

# Count the number of null values
dataset.isnull().sum()

merishnaSuwal

# Displaying the categories in different columns
print("Unique categories in the column 'pregnant'", dataset['pregnant'].unique())
print("Count of categories in the column 'pregnant' \n", dataset["pregnant"].value_counts())

print("\nUnique categories in the column 'T3 measured'", dataset['T3_measured'].unique())
print("Count of categories in the column 'T3 measured' \n", dataset["T3_measured"].value_counts())

print("\nUnique categories in the column 'Gender'", dataset['Gender'].unique())
print("Count of categories in the column 'Gender' \n", dataset["Gender"].value_counts())

merishnaSuwal

# mapping the values into binary
dataset["target"] = dataset["target"].map({"negative":0,"hypothyroid":1})

merishnaSuwal

# Renaming the first column as target
dataset = dataset.rename(columns = {dataset.columns[0]:"target"})

# Check the count of data in target
dataset["target"].value_counts()

merishnaSuwal

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:

merishnaSuwal

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
    ---------

merishnaSuwal

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

merishnaSuwal

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