104 lines
2.9 KiB
Python
104 lines
2.9 KiB
Python
from __future__ import print_function
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import tensorflow as tf
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import numpy as np
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import os
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import json
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import base64
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from io import BytesIO
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from PIL import Image
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##############################################
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# helper functions
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##############################################
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def build_model(x, y_, keep_prob):
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def weight_variable(shape):
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initial = tf.truncated_normal(shape, stddev=0.1)
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return tf.Variable(initial)
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def bias_variable(shape):
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initial = tf.constant(0.1, shape=shape)
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return tf.Variable(initial)
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def conv2d(x, W):
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return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
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def max_pool_2x2(x):
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return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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W_conv1 = weight_variable([5, 5, 1, 32])
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b_conv1 = bias_variable([32])
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x_image = tf.reshape(x, [-1, 28, 28, 1])
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h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
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h_pool1 = max_pool_2x2(h_conv1)
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W_conv2 = weight_variable([5, 5, 32, 64])
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b_conv2 = bias_variable([64])
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h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
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h_pool2 = max_pool_2x2(h_conv2)
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W_fc1 = weight_variable([7 * 7 * 64, 1024])
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b_fc1 = bias_variable([1024])
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h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
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h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
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h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
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W_fc2 = weight_variable([1024, 10])
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b_fc2 = bias_variable([10])
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y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
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return y_conv
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def base64ToImg(base64ImgString):
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if base64ImgString.startswith('b\''):
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base64ImgString = base64ImgString[2:-1]
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base64Img = base64ImgString.encode('utf-8')
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decoded_img = base64.b64decode(base64Img)
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img_buffer = BytesIO(decoded_img)
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img = Image.open(img_buffer)
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return img
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##############################################
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# API init() and run() methods
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##############################################
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def init():
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global x, keep_prob, y_conv, sess
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g = tf.Graph()
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with g.as_default():
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x = tf.placeholder(tf.float32, shape=[None, 784])
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y_ = tf.placeholder(tf.float32, shape=[None, 10])
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keep_prob = tf.placeholder(tf.float32)
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y_conv = build_model(x, y_, keep_prob)
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saver = tf.train.Saver()
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init_op = tf.global_variables_initializer()
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model_dir = os.path.join('sample_projects', 'outputs')
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saved_model_path = os.path.join(model_dir, 'model.ckpt')
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sess = tf.Session(graph=g)
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sess.run(init_op)
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saver.restore(sess, saved_model_path)
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def run(input_data):
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img = base64ToImg(json.loads(input_data)['data'])
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img_data = np.array(img, dtype=np.float32).flatten()
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img_data.resize((1, 784))
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y_pred = sess.run(y_conv, feed_dict={x: img_data, keep_prob: 1.0})
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predicted_label = np.argmax(y_pred[0])
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outJsonString = json.dumps({"label": str(predicted_label)})
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return str(outJsonString)
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