def _initialize(self, interactions):
self._num_items = interactions.num_items
self._num_users = interactions.num_users
self.test_sequence = interactions.test_sequences
self._net = Model3(self._num_users,
self._num_items,
self.model_args).to(self._device)
self._optimizer = optim.Adam(self._net.parameters(),
weight_decay=self._l2,
lr=self._learning_rate)
tf.cast(tf.equal(tf.argmax(predictions1, 1), tf.argmax(model1.Y, 1)),
tf.float32))
# Make model 2
model2 = Model2(X2, Y2, keep_prob2)
logits2, predictions2 = model2.build()
loss_op2 = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits2, labels=model2.Y2))
train_op2 = tf.train.AdamOptimizer(
learning_rate=model2.learning_rate).minimize(loss_op2)
accuracy2 = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(predictions2, 1), tf.argmax(model2.Y2, 1)),
tf.float32))
# Make model 3
model3 = Model3(X3, Y3, keep_prob3)
logits3, predictions3 = model3.build()
loss_op3 = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits3, labels=model3.Y3))
train_op3 = tf.train.AdamOptimizer(
learning_rate=model3.learning_rate).minimize(loss_op3)
accuracy3 = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(predictions3, 1), tf.argmax(model3.Y3, 1)),
tf.float32))
# # Make model 4
model4 = Model4(logitse1, logitse2, Y4)
logits4, predictions4 = model4.build()
loss_op4 = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits4, labels=model4.Y4))
train_op4 = tf.train.AdamOptimizer(