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tf_cifar10.py
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tf_cifar10.py
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import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
LOGDIR = "/tmp/cifar_classifier"
def cnn_model_fn(features, labels, mode):
"""Model function for CNN"""
# Input Layer
input_layer = tf.reshape(features["x"], [-1, 32, 32, 3])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
# learn a set of 32 filters
filters=32,
# slide a 3x3 receptive field across the input
kernel_size=[3, 3],
# pad the edges of the output tensors to retain original 32x32 input shape
padding="same",
# use the relu activation function
activation=tf.nn.relu)
# Convolutional Layer #2
conv2 = tf.layers.conv2d(
inputs=conv1,
filters=64,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(
inputs=conv2,
pool_size=[2, 2],
strides=[2, 2])
# Dropout Layer #1
dropout1 = tf.layers.dropout(
inputs=pool1,
rate=0.25,
training=mode == tf.estimator.ModeKeys.TRAIN)
# Conv Layer #3
conv3 = tf.layers.conv2d(
inputs=dropout1,
filters=64,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu)
# Conv Layer #4
conv4 = tf.layers.conv2d(
inputs=conv3,
filters=64,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #2
pool2 = tf.layers.max_pooling2d(
inputs=conv4,
pool_size=[2, 2],
strides=[2, 2])
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 8 * 8 * 64])
dense = tf.layers.dense(
inputs=pool2_flat,
units=1024,
activation=tf.nn.relu)
dropout2 = tf.layers.dropout(
inputs=dense,
rate=0.25,
training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits Layer
logits = tf.layers.dense(inputs=dropout2, 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.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):
# Load training and eval data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# Max scale all the image data
training_scaled = x_train / x_train.max()
test_scaled = x_test / x_test.max()
training_scaled = training_scaled.astype(dtype=np.float32)
test_scaled = test_scaled.astype(dtype=np.float32)
train_labels = np.asarray(y_train, dtype=np.int32)
eval_labels = np.asarray(y_test, dtype=np.int32)
cifar_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir=LOGDIR)
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": training_scaled},
y=train_labels,
batch_size=32,
num_epochs=5,
shuffle=True)
cifar_classifier.train(
input_fn=train_input_fn,
hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": test_scaled},
y=eval_labels,
num_epochs=1,
shuffle=False)
eval_results = cifar_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
if __name__ == "__main__":
tf.app.run(
main=None,
argv=None)
print('To run tensorboard, open up either Firefox or Chrome and type localhost:6006 in the address bar.')
print('Then run `tensorboard --logdir=%s` in your terminal.' % LOGDIR)
print('If youre on a Mac, provide the following flag: '
'--host=localhost to the previous terminal string.')