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cnn.py
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cnn.py
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"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from preprocessing import load_data
import os
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
NUM_OF_CLASSES = 6
DEFAULT_SIZE = 36
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
input_layer = tf.reshape(features["x"], [-1, DEFAULT_SIZE, DEFAULT_SIZE, 1])
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=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)
conv3 = tf.layers.conv2d(
inputs=pool2,
filters=128,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)
pool2_flat = tf.reshape(pool3, [-1, 4 * 4 * 128])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
#dense1 = tf.layers.dense(inputs=dense, units=256, activation=tf.nn.sigmoid)
# Add dropout operation; 0.6 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense, rate=0.2, training=mode == tf.estimator.ModeKeys.TRAIN)
logits = tf.layers.dense(inputs=dropout, units=NUM_OF_CLASSES)
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")
}
# 4. Create export outputs
export_outputs = {"predicted": tf.estimator.export.PredictOutput(predictions)}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions, export_outputs=export_outputs)
# 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.AdamOptimizer(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)
def main(unused_argv):
ROOT_PATH = "." # Denotes the current working directory
TRAIN_DATA_DIRECTORY = os.path.join(ROOT_PATH, "/root/leaf_image/DATA/training")
TEST_DATA_DIRECTORY = os.path.join(ROOT_PATH, "/root/leaf_image/DATA/testing")
train_data, train_labels = load_data(TRAIN_DATA_DIRECTORY)
eval_data, eval_labels = load_data(TEST_DATA_DIRECTORY)
# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="./tmp/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
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
mnist_classifier.train(
input_fn=train_input_fn,
steps=100,
hooks=[logging_hook])
def serving_input_receiver_fn():
"""Build the serving inputs."""
inputs = {"x": tf.placeholder(shape=[1, DEFAULT_SIZE, DEFAULT_SIZE, 1], dtype=tf.float32)}
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
export_dir = mnist_classifier.export_savedmodel(
export_dir_base="./model_saved/",
serving_input_receiver_fn=serving_input_receiver_fn)
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": eval_data},
y=eval_labels,
num_epochs=2,
shuffle=False)
print(eval_input_fn)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": train_data[0]},
shuffle=False)
prediction_results = mnist_classifier.predict(predict_input_fn)
for i in prediction_results:
print(i)
print(i['classes'])
if __name__ == "__main__":
tf.app.run()