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train.py

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+import tensorflow.compat.v1 as tf
+import model
+import utils
+import read_img
+
+
+def get_loss(result, y):
+    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=result, labels=y)
+    return tf.reduce_mean(cross_entropy)
+
+
+def get_optimizer(loss):
+    train_variables = tf.trainable_variables()
+    optimizer = tf.train.AdamOptimizer(learning_rate=utils.lr).minimize(loss, var_list=train_variables)
+    return optimizer
+
+
+def train():
+    x_train, y_train, _ = read_img.read_flowers(True)
+    x_test, y_test, _ = read_img.read_flowers(False)
+
+    train_batches = x_train.shape[0]
+
+    x, y, one_hot, result = model.get_model(is_train=True)
+    loss = get_loss(result, one_hot)
+    optimizer = get_optimizer(loss)
+
+    saver = tf.train.Saver()
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+
+        for epoch in range(utils.epochs):
+            for batch in range(train_batches // utils.batch_size):
+                start = batch * utils.batch_size
+                next_x = x_train[start:start + utils.batch_size]
+                next_y = y_train[start:start + utils.batch_size]
+
+                sess.run(optimizer, feed_dict={x: next_x, y: next_y})
+
+            loss_result = sess.run(loss, feed_dict={x: x_test, y: y_test})
+            print("epoch: {}, loss: {}".format(epoch, loss_result))
+
+        saver.save(sess, "./result/result.ckpt")
+
+
+if __name__ == '__main__':
+    train()