def init():
checkpoint_manager = CheckpointManager(NETWORK_NUMBER)
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
with tf.Session() as ses:
ses.run(init_g)
ses.run(init_l)
checkpoint_manager.restore_model(ses)
sess = ses
def test_nn(number,
input_placeholder,
output_placeholder,
accuracy,
cost,
limit=None):
checkpoint_manager = CheckpointManager(number)
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
with tf.Session() as sess:
sess.run(init_g)
sess.run(init_l)
checkpoint_manager.restore_model(sess)
counter = 0
total_accuracy = 0
total_cost = 0
for test_images, test_labels in ds.test_batch_generator(BATCH_SIZE):
feed = {
input_placeholder: test_images,
output_placeholder: test_labels
}
test_accuracy, test_cost = sess.run([accuracy, cost],
feed_dict=feed)
print("Batch {:3}, Accuracy: {:3.1%}, Cost: {}" \
.format(counter, test_accuracy, test_cost))
total_accuracy += test_accuracy
total_cost += test_cost
counter += 1
overall_accuracy = total_accuracy / counter
overall_cost = total_cost / counter
print("Total test accuracy: {:5.1%}".format(overall_accuracy))
return overall_accuracy, overall_cost
def test_nn(number, input_placeholder, output_placeholder, accuracy, cost):
checkpoint_manager = CheckpointManager(number)
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
with tf.Session() as sess:
sess.run(init_g)
sess.run(init_l)
checkpoint_manager.restore_model(sess)
total_accuracy = 0
total_cost = 0
batches = None
for batch_index, test_images, test_labels in ds.test_batch_generator(100, grayscale=True):
feed = {
input_placeholder: test_images,
output_placeholder: test_labels
}
test_accuracy, test_cost = sess.run(
[accuracy, cost], feed_dict=feed)
print("Batch {:3}, Accuracy: {:3.1%}, Cost: {}" \
.format(batch_index, test_accuracy, test_cost))
total_accuracy += test_accuracy
total_cost += test_cost
batches = batch_index
overall_accuracy = total_accuracy / (batches + 1)
overall_cost = total_cost / (batches + 1)
print("Total test accuracy: {:5.1%}".format(overall_accuracy))
return overall_accuracy, overall_cost
def main():
tf.reset_default_graph()
NETWORK_NUMBER = 4
input_placeholder = tf.placeholder(
tf.float32, shape=[None, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNEL], name='input_placeholder')
output_placeholder = tf.placeholder(
tf.float32, shape=[None, 1], name='output_placeholder')
layer_conv_1, weights_conv_1 = new_conv_layer(
input=input_placeholder,
num_input_channels=IMAGE_CHANNEL,
filter_size=5,
num_filters=64,
pooling=2
)
layer_conv_2, weights_conv_2 = new_conv_layer(
input=layer_conv_1,
num_input_channels=64,
filter_size=3,
num_filters=128,
pooling=2
)
layer_conv_3, weights_conv_3 = new_conv_layer(
input=layer_conv_2,
num_input_channels=128,
filter_size=3,
num_filters=128,
pooling=None
)
layer_conv_4, weights_conv_4 = new_conv_layer(
input=layer_conv_3,
num_input_channels=128,
filter_size=3,
num_filters=128,
pooling=None
)
layer_conv_5, weights_conv_5 = new_conv_layer(
input=layer_conv_4,
num_input_channels=128,
filter_size=3,
num_filters=256,
pooling=3
)
layer_flat, num_features = flatten_layer(layer_conv_5)
layer_fc_1 = new_fc_layer(
input=layer_flat, num_inputs=num_features, num_outputs=4096)
layer_fc_1 = tf.nn.sigmoid(layer_fc_1)
layer_fc_2 = new_fc_layer(
input=layer_fc_1, num_inputs=4096, num_outputs=4096)
layer_fc_2 = tf.nn.sigmoid(layer_fc_2)
layer_output = new_fc_layer(
input=layer_fc_2, num_inputs=4096, num_outputs=1)
layer_output = tf.nn.sigmoid(layer_output)
checkpoint_manager = CheckpointManager(NETWORK_NUMBER)
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
with tf.Session() as sess:
sess.run(init_g)
sess.run(init_l)
checkpoint_manager.restore_model(sess)
imgs = []
for file in os.listdir("./"):
if file.endswith(".png"):
path = os.path.join("./", file)
im = Image.open(path)
imgs.append(np.array(im).reshape(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNEL))
print(layer_output.eval(feed_dict={input_placeholder: imgs},
session=sess))