Layers tutorial and corresponding example code.

Change: 144031391

2 files changed, 194 insertions, 0 deletions

diff --git a/tensorflow/examples/tutorials/layers/BUILD b/tensorflow/examples/tutorials/layers/BUILD
new file mode 100644
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+++ b/tensorflow/examples/tutorials/layers/BUILD
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+# Example Estimator model
+
+package(
+    default_visibility = ["//visibility:public"],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+py_binary(
+    name = "cnn_mnist",
+    srcs = [
+        "cnn_mnist.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow:tensorflow_py",
+    ],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/examples/tutorials/layers/cnn_mnist.py b/tensorflow/examples/tutorials/layers/cnn_mnist.py
new file mode 100644
index 0000000000..09dbffd517
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+++ b/tensorflow/examples/tutorials/layers/cnn_mnist.py
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+#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.contrib import learn
+from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+
+def cnn_model_fn(features, labels, mode):
+  """Model function for CNN."""
+  # Input Layer
+  # Reshape X to 4-D tensor: [batch_size, width, height, channels]
+  # MNIST images are 28x28 pixels, and have one color channel
+  input_layer = tf.reshape(features, [-1, 28, 28, 1])
+
+  # Convolutional Layer #1
+  # Computes 32 features using a 5x5 filter with ReLU activation.
+  # Padding is added to preserve width and height.
+  # Input Tensor Shape: [batch_size, 28, 28, 1]
+  # Output Tensor Shape: [batch_size, 28, 28, 32]
+  conv1 = tf.layers.conv2d(
+      inputs=input_layer,
+      filters=32,
+      kernel_size=[5, 5],
+      padding="same",
+      activation=tf.nn.relu)
+
+  # Pooling Layer #1
+  # First max pooling layer with a 2x2 filter and stride of 2
+  # Input Tensor Shape: [batch_size, 28, 28, 32]
+  # Output Tensor Shape: [batch_size, 14, 14, 32]
+  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
+
+  # Convolutional Layer #2
+  # Computes 64 features using a 5x5 filter.
+  # Padding is added to preserve width and height.
+  # Input Tensor Shape: [batch_size, 14, 14, 32]
+  # Output Tensor Shape: [batch_size, 14, 14, 64]
+  conv2 = tf.layers.conv2d(
+      inputs=pool1,
+      filters=64,
+      kernel_size=[5, 5],
+      padding="same",
+      activation=tf.nn.relu)
+
+  # Pooling Layer #2
+  # Second max pooling layer with a 2x2 filter and stride of 2
+  # Input Tensor Shape: [batch_size, 14, 14, 64]
+  # Output Tensor Shape: [batch_size, 7, 7, 64]
+  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
+
+  # Flatten tensor into a batch of vectors
+  # Input Tensor Shape: [batch_size, 7, 7, 64]
+  # Output Tensor Shape: [batch_size, 7 * 7 * 64]
+  pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
+
+  # Dense Layer
+  # Densely connected layer with 1024 neurons
+  # Input Tensor Shape: [batch_size, 7 * 7 * 64]
+  # Output Tensor Shape: [batch_size, 1024]
+  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
+
+  # Add dropout operation; 0.6 probability that element will be kept
+  dropout = tf.layers.dropout(
+      inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
+
+  # Logits layer
+  # Input Tensor Shape: [batch_size, 1024]
+  # Output Tensor Shape: [batch_size, 10]
+  logits = tf.layers.dense(inputs=dropout, units=10)
+
+  loss = None
+  train_op = None
+
+  # Calculate Loss (for both TRAIN and EVAL modes)
+  if mode != learn.ModeKeys.INFER:
+    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
+    loss = tf.losses.softmax_cross_entropy(
+        onehot_labels=onehot_labels, logits=logits)
+
+  # Configure the Training Op (for TRAIN mode)
+  if mode == learn.ModeKeys.TRAIN:
+    train_op = tf.contrib.layers.optimize_loss(
+        loss=loss,
+        global_step=tf.contrib.framework.get_global_step(),
+        learning_rate=0.001,
+        optimizer="SGD")
+
+  # Generate Predictions
+  predictions = {
+      "classes": tf.argmax(
+          input=logits, axis=1),
+      "probabilities": tf.nn.softmax(
+          logits, name="softmax_tensor")
+  }
+
+  # Return a ModelFnOps object
+  return model_fn_lib.ModelFnOps(
+      mode=mode, predictions=predictions, loss=loss, train_op=train_op)
+
+
+def main(unused_argv):
+  # Load training and eval data
+  mnist = learn.datasets.load_dataset("mnist")
+  train_data = mnist.train.images  # Returns np.array
+  train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
+  eval_data = mnist.test.images  # Returns np.array
+  eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
+
+  # Create the Estimator
+  mnist_classifier = learn.Estimator(
+      model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
+
+  # Set up logging for predictions
+  # Log the values in the "Softmax" tensor with label "probabilities"
+  tensors_to_log = {"probabilities": "softmax_tensor"}
+  logging_hook = tf.train.LoggingTensorHook(
+      tensors=tensors_to_log, every_n_iter=50)
+
+  # Train the model
+  mnist_classifier.fit(
+      x=train_data,
+      y=train_labels,
+      batch_size=100,
+      steps=20000,
+      monitors=[logging_hook])
+
+  # Configure the accuracy metric for evaluation
+  metrics = {
+      "accuracy":
+          learn.metric_spec.MetricSpec(
+              metric_fn=tf.metrics.accuracy, prediction_key="classes"),
+  }
+
+  # Evaluate the model and print results
+  eval_results = mnist_classifier.evaluate(
+      x=eval_data, y=eval_labels, metrics=metrics)
+  print(eval_results)
+
+
+if __name__ == "__main__":
+  tf.app.run()