def scao_model_iv(AI_saved_dir, arti_flux_678, arti_label_678):
#-----------------------------------
# Load AI
print('Loading AI...')
width_of_data = 1
img_maj = 3
image_shape = (width_of_data, img_maj)
kernal_shape = (width_of_data, 2)
num_kernal_1 = 32
num_kernal_2 = 64
num_conn_neural = 100
num_label = 3
#-----------------------------------
# Construct an AI
tf.reset_default_graph()
x = tf.placeholder(tf.float32, [None, width_of_data * img_maj], name='x')
y_true = tf.placeholder(tf.float32, [None, 3], name='y_true')
y_true_cls = tf.argmax(y_true, axis=1)
x_image = tf.reshape(x, [-1, image_shape[0], image_shape[1], 1])
# First layer( First kernal)
W_conv1 = weight_variable(
[kernal_shape[0], kernal_shape[1], 1, num_kernal_1])
b_conv1 = bias_variable([num_kernal_1])
h_conv1 = tf.nn.selu(
tf.nn.conv2d(x_image, W_conv1, [1, 1, 1, 1], 'SAME') + b_conv1)
# Second layer( Second kernal)
W_conv2 = weight_variable(
[kernal_shape[0], kernal_shape[1], num_kernal_1, num_kernal_2])
b_conv2 = bias_variable([num_kernal_2])
h_conv2 = tf.nn.selu(
tf.nn.conv2d(h_conv1, W_conv2, [1, 1, 1, 1], 'SAME') + b_conv2)
# Third layer ( Fully connected)
W_fc1 = weight_variable(
[image_shape[0] * image_shape[1] * num_kernal_2, num_conn_neural])
b_fc1 = bias_variable([num_conn_neural])
h_conv2_flat = tf.reshape(
h_conv2, [-1, image_shape[0] * image_shape[1] * num_kernal_2])
h_fc1 = tf.nn.selu(tf.matmul(h_conv2_flat, W_fc1) + b_fc1)
# Output layer
W_fc2 = weight_variable([num_conn_neural, num_label])
b_fc2 = bias_variable([num_label])
layer_last = tf.matmul(h_fc1, W_fc2) + b_fc2
y_pred = tf.nn.softmax(layer_last)
y_pred_cls = tf.argmax(y_pred, axis=1)
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Saver
saver = tf.train.Saver()
print("AI:{0}".format(AI_saved_dir))
if not os.path.exists(AI_saved_dir):
print("No AI can be restore, please check folder ./checkpoints")
exit(1)
save_path = os.path.join(AI_saved_dir, 'best_validation')
session = tf.Session()
# Restore previous weight
saver.restore(sess=session, save_path=save_path)
#-----------------------------------
# Make a prediction
def predict_label(images, labels):
# Number of images.
num_images = len(images)
# initialize
label_pred = np.zeros(num_images * 3).reshape((num_images, 3))
feed_dict = {x: images[:], y_true: labels[:]}
# process
label_pred = session.run(y_pred, feed_dict=feed_dict)
return label_pred
label_pred_678 = predict_label(arti_flux_678, arti_label_678)
#-----------------------------------
# Close session
session.close()
return label_pred_678
kernal_shape = (width_of_data, 2)
num_kernal_1 = 32
num_kernal_2 = 64
num_conn_neural = 100
num_label = 3
#-----------------------------------
# Construct an AI
tf.reset_default_graph()
x = tf.placeholder(tf.float32, [None, width_of_data * img_maj], name='x')
y_true = tf.placeholder(tf.float32, [None, 3], name='y_true')
y_true_cls = tf.argmax(y_true, axis=1)
x_image = tf.reshape(x, [-1, image_shape[0], image_shape[1], 1])
# First layer( First kernal)
W_conv1 = weight_variable(
[kernal_shape[0], kernal_shape[1], 1, num_kernal_1])
b_conv1 = bias_variable([num_kernal_1])
h_conv1 = tf.nn.selu(
tf.nn.conv2d(x_image, W_conv1, [1, 1, 1, 1], 'SAME') + b_conv1)
# Second layer( Second kernal)
W_conv2 = weight_variable(
[kernal_shape[0], kernal_shape[1], num_kernal_1, num_kernal_2])
b_conv2 = bias_variable([num_kernal_2])
h_conv2 = tf.nn.selu(
tf.nn.conv2d(h_conv1, W_conv2, [1, 1, 1, 1], 'SAME') + b_conv2)
# Third layer ( Fully connected)
W_fc1 = weight_variable(
[image_shape[0] * image_shape[1] * num_kernal_2, num_conn_neural])
b_fc1 = bias_variable([num_conn_neural])
h_conv2_flat = tf.reshape(
h_conv2, [-1, image_shape[0] * image_shape[1] * num_kernal_2])
h_fc1 = tf.nn.selu(tf.matmul(h_conv2_flat, W_fc1) + b_fc1)