1，tf.layers基础函数

  • conv2d(). Constructs a two-dimensional convolutional layer. Takes number of filters, filter kernel size, padding, and activation function as arguments.
  • max_pooling2d(). Constructs a two-dimensional pooling layer using the max-pooling algorithm. Takes pooling filter size and stride as arguments.
  • dense(). Constructs a dense layer. Takes number of neurons and activation function as arguments.

2，cnn_model_fn function, which conforms to the interface expected by TensorFlow's Estimator API

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=32,
kernel_size=[5, 5],
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=64,
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 * 64])
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)

predictions = {
# Generate predictions (for PREDICT and EVAL mode)
"classes": tf.argmax(input=logits, axis=1),
# Add `softmax_tensor` to the graph. It is used for PREDICT and by the
# `logging_hook`.
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}

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)

3，input layer

Here, our MNIST dataset is composed of monochrome 28x28 pixel images, so the desired shape for our input layer is [batch_size, 28, 28, 1].

Note that we've indicated -1 for batch size, which specifies that this dimension should be dynamically computed based on the number of input values in features["x"], holding the size of all other dimensions constant. This allows us to treat batch_size as a hyperparameter that we can tune. For example, if we feed examples into our model in batches of 5, features["x"] will contain 3,920 values (one value for each pixel in each image), and input_layer will have a shape of [5, 28, 28, 1]. Similarly, if we feed examples in batches of 100, features["x"] will contain 78,400 values, and input_layer will have a shape of [100, 28, 28, 1]

4，创建Estimator

# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")

5，Set Up a Logging Hook

# Set up logging for predictions
tensors_to_log = {"probabilities": "softmax_tensor"}

logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)

6，输入数据，开始训练

# 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=1,
hooks=[logging_hook])

mnist_classifier.train(input_fn=train_input_fn, steps=1000)

7，路径

train_input_fn-》mnist_classifier-》cnn_model_fn，然后input_fn与input_layer对接？

8，评估

eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)

eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)