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fit()
import tensorflow as tf import numpy as np import deep_tempering as dt from importlib import reload reload(dt.training_utils) reload(dt.callbacks) tf.compat.v1.keras.backend.clear_session() def model_builder(hp): inputs = tf.keras.layers.Input((2,)) res = tf.keras.layers.Dense(2, activation=tf.nn.relu)(inputs) dropout_rate = hp.get_hparam('dropout_rate', default_value=0.0) res = tf.keras.layers.Dropout(dropout_rate)(res) res = tf.keras.layers.Dense(2, activation=tf.nn.softmax)(res) model = tf.keras.models.Model(inputs, res) return model n_replicas = 6 model = dt.EnsembleModel(model_builder) hp = { 1: {'learning_rate': np.ones((n_replicas, )) * 0.1, # starting at step 1 'dropout_rate': np.zeros((n_replicas,))}, 10: {'learning_rate': np.linspace(0.01, 0.001, n_replicas), # starting at step 200 'dropout_rate': np.linspace(0, 0.5, n_replicas)} } model.compile(optimizer=tf.keras.optimizers.SGD(), loss='sparse_categorical_crossentropy', metrics=['accuracy'], n_replicas=n_replicas) x = np.random.normal(0, 1, (20, 2)) y = np.random.randint(0, 2, (20,)) history = model.fit(x, y, hyper_params=hp, batch_size=2, epochs=15, swap_step=3, callbacks=[dt.callbacks.MetropolisExchangeCallback((x, y), 3, 5)], burn_in=0, verbose=0)
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