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# 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() |
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# Dropping the TBG column as it contains extremely high number of null values | |
dataset.drop('TBG', axis = 1, inplace=True) |
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# Replacing ? into NaN values | |
dataset.replace(to_replace='?', inplace=True, value=np.NaN) | |
# Count the number of null values | |
dataset.isnull().sum() |
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# 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()) |
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# mapping the values into binary | |
dataset["target"] = dataset["target"].map({"negative":0,"hypothyroid":1}) |
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# 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() |
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show_images = 5 | |
# Plot the images from last epoch | |
data_images = helper.get_batch(glob(os.path.join("./generated_images/epoch_" + str(num_epochs-1) +"/", '*.jpg'))[:show_images], 64, 64, 'RGB') | |
plt.imshow(helper.images_square_grid(data_images, 'RGB')) |
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# Size of latent (noise) vector to generator | |
z_dim = 100 | |
# Learning ratess | |
learning_rate_D = .00005 | |
learning_rate_G = 2e-4 | |
# Batch size | |
batch_size = 4 | |
# Number of epochs |
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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 |
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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 |