#%% Train the model
# with tf.device('/gpu:0'):
train(
m1,
X_train,
y_train,
X_test,
y_test,
num=100,
samples=1,
epochs=1000,
iter_train=1,
num_inference=100,
save="model_w",
batch=True,
temperature_function=lambda x: exponential_multiplicative_cooling(x, 1.0, 0.5, 0.99),
save_results="./gumblevae_results.txt",
beta_z_method=z_cooling,
beta_y_method=y_cooling,
beta_d_method=d_cooling,
)
#%%
qy_g_x__logit, qy_g_x__prob = m1.graph_qy_g_x(X_train[[9]])
qy_g_x__ohe = np.array([m1.graph_qy_g_x_ohe(qy_g_x__prob, 0.005).numpy()[0] for i in range(1000)])
#%%
logit_df = pd.DataFrame()
for col in range(qy_g_x__ohe.shape[1]):
temp_df = pd.DataFrame({"value": qy_g_x__ohe[:, col]})
def train_test_model(run_id, hparams, X_train, y_train, X_test, y_test):
# hp.hparams(hparams) # record the values used in this trial
seed = hparams[HP_seed]
tf.random.set_seed(seed)
params = {
"components": hparams[HP_components],
"input_dimension": X_train.shape[1],
"embedding_dimensions": eval(hparams[HP_encoder_dims])[0],
"latent_dimensions": eval(hparams[HP_encoder_dims])[1],
"mixture_embedding_dimensions": eval(hparams[HP_mixture_dims])[0],
"mixture_latent_dimensions": eval(hparams[HP_mixture_dims])[1],
"embedding_activations": tf.nn.relu,
"kind": "binary",
"learning_rate": 1.0,
"gradient_clip": None,
"bn_before": True if hparams[HP_bn] == "before" else False,
"bn_after": True if hparams[HP_bn] == "after" else False,
"categorical_epsilon": 0.0,
"reconstruction_epsilon": 0.0,
"latent_epsilon": 0.0,
"latent_prior_epsilon": 0.0,
"z_kl_lambda": 1.0,
"c_kl_lambda": 1.0,
"cat_latent_bias_initializer": None,
"connected_weights": hparams[HP_connected_weights],
# "optimizer":tf.keras.optimizers.Adam(lr_schedule, epsilon=1e-16),
"optimizer": tf.keras.optimizers.Adam(1e-3, epsilon=1e-16),
"categorical_latent_embedding_dropout": 0.2,
"mixture_latent_mu_embedding_dropout": 0.2,
"mixture_latent_var_embedding_dropout": 0.2,
"mixture_posterior_mu_dropout": 0.2,
"mixture_posterior_var_dropout": 0.2,
"recon_dropouut": 0.2,
#'latent_fixed_var': 0.01,
}
z_cooling = lambda: 1.0
y_cooling = lambda: 1.0
m1 = model.Gmvae(**params)
params["embedding_activations"] = "relu"
params["optimizer"] = "adam_1e-3_1e-9"
param_string = (
"/seed__" + str(seed) + "/" +
"/".join([str(k) + "_" + str(v) for k, v in params.items()]))
train(
m1,
X_train,
y_train,
X_test,
y_test,
num=100,
samples=hparams[HP_samples],
epochs=110,
iter_train=1,
num_inference=1000,
save="model_w_5",
batch=True,
temperature_function=lambda x: exponential_multiplicative_cooling(
x, 1.0, 0.5, 0.99),
# temperature_function = lambda x: 0.1
save_results="./gumble_results.txt",
beta_z_method=z_cooling,
beta_y_method=y_cooling,
tensorboard=run_id,
)
idx_tr = m1.predict(X_train).numpy().argmax(1)
idx_te = m1.predict(X_test).numpy().argmax(1)
ami_tr = adjusted_mutual_info_score(y_train,
idx_tr,
average_method="arithmetic")
ami_te = adjusted_mutual_info_score(y_test,
idx_te,
average_method="arithmetic")
attch_te = np.array(np.unique(idx_te,
return_counts=True)[1]).max() / len(idx_te)
purity_train = purity_score(y_train, idx_tr)
purity_test = purity_score(y_test, idx_te)
return ami_tr, ami_te, purity_train, purity_test