Reputation: 3053
In this tutorial, they successfully log the softmax function by giving a name to the tf.nn.softmax
node.
tf.nn.softmax(logits, name="softmax_tensor") # giving name to the node
.
.
.
tensors_to_log = {"probadfabilities": "softmax_tensor"} # logging the node
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
Now, instead of the softmax, I would like to also log the output of the last Dense layer.
logits = tf.layers.dense(inputs=dropout, units=10, name='logits')
.
.
.
tensors_to_log = {"last_layer": "logits"}
But it gives me the following error
KeyError: "The name 'logits:0' refers to a Tensor which does not exist. The operation, 'logits', does not exist in the graph."
My question is: how to log the layer output in tensorflow?
import tensorflow as tf
import numpy as np
import os
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=128,
kernel_size=[7, 7],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=256,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 256])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=10, name='logits')
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# Add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])
}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
((train_data, train_labels),
(eval_data, eval_labels)) = tf.keras.datasets.mnist.load_data()
train_data = train_data/np.float32(255)
train_labels = train_labels.astype(np.int32) # not required
eval_data = eval_data/np.float32(255)
eval_labels = eval_labels.astype(np.int32)
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="./mnist_convnet_model")
# Set up logging for predictions
tensors_to_log = {"last_layer": "logits"}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)
# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
# train one step and display the probabilties
mnist_classifier.train(
input_fn=train_input_fn,
steps=10,
hooks=[logging_hook])
Upvotes: 1
Views: 871
Reputation: 1196
In the tf.official ResNet implementation, they use tf.identity
for this purpose:
logits = tf.identity(logits, 'logits')
Upvotes: 2