I got error:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'adanet/iteration_0/subnetwork_t0_2_layer_dnn/input_2' with dtype float and shape [?,1,98]
[[{{node adanet/iteration_0/subnetwork_t0_2_layer_dnn/input_2}} = Placeholder[dtype=DT_FLOAT, shape=[?,1,98], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Hello, i`m trying to create more complex NN. Help please, what can be wrong?
Here is code: `class _SimpleCNNBuilder(adanet.subnetwork.Builder):
def init(self, optimizer, layer_size, num_layers, learn_mixture_weights, seed):
def build_subnetwork(self, features, logits_dimension, training, iteration_step, summary, previous_ensemble=None):
def _measure_complexity(self): return tf.sqrt(tf.to_float(self._num_layers))
def build_subnetwork_train_op(self, subnetwork, loss, var_list, labels, iteration_step, summary, previous_ensemble): return self._optimizer.minimize(loss=loss, var_list=var_list)
def build_mixture_weights_train_op(self, loss, var_list, logits, labels, iteration_step, summary):
@property def name(self):
class SimpleCNNGenerator(adanet.subnetwork.Generator):
def init(self, optimizer, layer_size=64, learn_mixture_weights=False, seed=None):
def generate_candidates(self, previous_ensemble, iteration_number, previous_ensemble_reports, all_reports):
LEARNING_RATE = 0.001 TRAIN_STEPS = 80000 BATCH_SIZE = 98
LEARN_MIXTURE_WEIGHTS = False ADANET_LAMBDA = 0 ADANET_ITERATIONS = 4
def train_and_evaluate(experiment_name, learn_mixture_weights=LEARN_MIXTURE_WEIGHTS, adanet_lambda=ADANET_LAMBDA):
model_dir = os.path.join(LOG_DIR, experiment_name)
estimator = adanet.Estimator( head=tf.contrib.estimator.regression_head( label_dimension=2, loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE),
train_spec = tf.estimator.TrainSpec( input_fn=input_fn("train", training=True, batch_size=BATCH_SIZE), max_steps=TRAIN_STEPS) eval_spec = tf.estimator.EvalSpec( input_fn=input_fn("test", training=False, batch_size=BATCH_SIZE), steps=None, start_delay_secs=1, throttle_secs=30, )
test = tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
print("Loss:", test[0]["average_loss"]) print("Architecture:", ensemble_architecture(test[0]))
return estimator`
I got error: InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'adanet/iteration_0/subnetwork_t0_2_layer_dnn/input_2' with dtype float and shape [?,1,98] [[{{node adanet/iteration_0/subnetwork_t0_2_layer_dnn/input_2}} = Placeholder[dtype=DT_FLOAT, shape=[?,1,98], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]