model name

vae.py

@@ -17,10 +17,12 @@
 from plotnine import *
 import itertools
 
-def load_data(input_file, columns):
+def load_data(input_file, columns, plotdata=True):
     df0 = pd.read_csv(input_file)
     cols = columns if columns is not None else df0.columns
     df = df0[cols]
+    if plotdata:
+        plot_sample(df, f"{model_name}_input_plot.png")
     onehotencodeddf = pd.get_dummies(df, columns=cols)
     return onehotencodeddf
 
@@ -83,11 +85,13 @@ def sample_decoder(decoder,
     df.to_csv(filename, index=False)
 
     if len(categorical_columns) >= 3:
-        df2 = df.groupby(list(df.columns)).size().reset_index(name="Frequency")
-        cols = list(df2.columns)
-        (ggplot(df2, aes(x = cols[1], y = "np.log(Frequency + 1)", color = cols[2])) + geom_point() + geom_line() + facet_grid(f"{cols[0]} ~ {cols[2]}")).save(f"{model_name}_samples_plot.png")
-
+        plot_sample(df, f"{model_name}_samples_plot.png")
+
 
+def plot_sample(df, plotfile):
+    df2 = df.groupby(list(df.columns)).size().reset_index(name="Frequency")
+    cols = list(df2.columns)
+    (ggplot(df2, aes(x = cols[1], y = "np.log(Frequency + 1)", color = cols[2])) + geom_point() + geom_line() + facet_grid(f"{cols[0]} ~ {cols[2]}")).save(plotfile)
 
 
 # MNIST dataset
@@ -141,7 +145,7 @@ def get_model(original_dim, scale_width, latent_dim, loss_function="xent"):
 
     # instantiate VAE model
     outputs = decoder(encoder(inputs)[2])
-    vae = Model(inputs, outputs, name='vae_mlp')
+    vae = Model(inputs, outputs, name=model_name)
     # VAE loss = mse_loss or xent_loss + kl_loss
     if loss_function == "mse":
         reconstruction_loss = mse(inputs, outputs)