def model(features, labels, mode, params):
"""CNN classifier model."""
images = features["image"]
labels = labels["label"]
training = mode == tf.estimator.ModeKeys.TRAIN
drop_rate = params.drop_rate if training else 0.0
features = ops.conv_layers(images,
filters=[64, 128, 256],
kernels=[3, 3, 3],
pool_sizes=[2, 2, 2])
features = tf.contrib.layers.flatten(features)
logits = ops.dense_layers(features, [512, params.num_classes],
drop_rates=drop_rate,
linear_top_layer=True)
predictions = tf.argmax(logits, axis=-1)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
tf.summary.image("images", images)
eval_metrics = {
"accuracy": tf.metrics.accuracy(labels, predictions),
"top_1_error": tf.metrics.mean(metrics.top_k_error(labels, logits, 1)),
}
tf.add_to_collection("batch_logging", tf.identity(labels, name="labels"))
tf.add_to_collection("batch_logging",
tf.identity(predictions, name="predictions"))
return {"predictions": predictions}, loss, eval_metrics
def model(features, labels, mode, params):
"""CNN classifier model."""
images = features["image"]
labels = labels["label"]
training = mode == tf.estimator.ModeKeys.TRAIN
features = ops.conv_layers(
images, [16], [3], linear_top_layer=True)
features = ops.resnet_blocks(
features, [16, 32, 64], [1, 2, 2], 5, training)
features = ops.batch_normalization(features, training=training)
features = tf.nn.relu(features)
features = tf.reduce_mean(features, axis=[1, 2])
logits = ops.dense_layers(
features, [params.num_classes], linear_top_layer=False)
predictions = tf.argmax(logits, axis=-1)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
tf.summary.image("images", images)
eval_metrics = {
"accuracy": tf.metrics.accuracy(labels, predictions),
"top_1_error": tf.metrics.mean(metrics.top_k_error(labels, logits, 1)),
}
return {"predictions": predictions}, loss, eval_metrics
def model_fn(features, labels, mode, params):
"""CNN classifier model."""
images = features['image']
labels = labels['label']
drop_rate = params.drop_rate if mode == tf.estimator.ModeKeys.TRAIN else 0.0
features = ops.conv_layers(
images,
filters=[32, 64, 128],
kernels=[3, 3, 3],
pools=[2, 2, 2])
features = tf.contrib.layers.flatten(features)
logits = ops.dense_layers(
features, [512, params.num_classes],
drop_rate=drop_rate,
linear_top_layer=True)
predictions = tf.argmax(logits, axis=1)
loss = tf.losses.sparse_softmax_cross_entropy(
labels=labels, logits=logits)
summary.labeled_image('images', images, predictions)
return {'predictions': predictions}, loss
def model(self, features, labels, mode, params):
"""CNN classifier model."""
images = features["image"]
labels = labels["label"]
training = mode == tf.estimator.ModeKeys.TRAIN
drop_rate = params.drop_rate if training else 0.0
images = tf.layers.dropout(images, params.input_drop_rate)
features = ops.conv_layers(
images,
filters=[96, 96, 192, 192, 192, 192, params.num_classes],
kernels=[3, 3, 3, 3, 3, 1, 1],
pool_sizes=[1, 3, 1, 3, 1, 1, 1],
pool_strides=[1, 2, 1, 2, 1, 1, 1],
drop_rates=[0, 0, drop_rate, 0, drop_rate, 0, 0],
batch_norm=True,
training=training,
pool_activation=tf.nn.relu,
linear_top_layer=True,
weight_decay=params.weight_decay)
logits = tf.reduce_mean(features, [1, 2])
predictions = tf.argmax(logits, axis=-1)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels,
logits=logits)
tf.summary.image("images", images)
eval_metrics = {
"accuracy": tf.metrics.accuracy(labels, predictions),
"top_1_error": tf.metrics.mean(ops.top_k_error(labels, logits, 1)),
}
return {"predictions": predictions}, loss, eval_metrics