EfficientNetV2L-fold1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.compat.v2 as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pathlib\n",
    "import random\n",
    "import glob\n",
    "import os\n",
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "import time\n",
    "keras = tf.keras\n",
    "config = ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_image(path):\n",
    "    image = tf.io.read_file(path)\n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    image = tf.image.resize(image, [224, 224])\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    image = image/255.0\n",
    "    image = 2*image-1\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = r'C:\\Users\\24306\\Desktop\\datasets\\kfold\\1\\train'\n",
    "test_dir  = r'C:\\Users\\24306\\Desktop\\datasets\\kfold\\1\\test'\n",
    "checkpoint_path = r'C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "<PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.float32, name=None))>\n"
     ]
    }
   ],
   "source": [
    "#打乱顺序\n",
    "train_root = pathlib.Path(train_dir)\n",
    "train_image_paths = list(train_root.glob('*/*'))\n",
    "train_image_paths = [str(path) for path in train_image_paths]\n",
    "random.shuffle(train_image_paths)\n",
    "#处理训练标签\n",
    "train_image_labels = [pathlib.Path(path).parent.name for path in train_image_paths]\n",
    "train_labels = [[label.split('_')[0]] for label in train_image_labels]\n",
    "new_train_labels=[]\n",
    "for n in train_labels:\n",
    "    new_train_labels.append(float(n[0]));  \n",
    "train_labels=new_train_labels\n",
    "print(train_labels)\n",
    "#处理训练图像\n",
    "train_path_ds = tf.data.Dataset.from_tensor_slices(train_image_paths)\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "train_image_ds = train_path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)\n",
    "train_label_ds = tf.data.Dataset.from_tensor_slices(train_labels)\n",
    "train_image_label_ds = tf.data.Dataset.zip((train_image_ds, train_label_ds))\n",
    "# image_count = len(all_image_paths)\n",
    "\n",
    "# test_count = int(image_count*0.2)\n",
    "train_count = len(train_image_paths)\n",
    "# train_data = image_label_ds.skip(test_count)\n",
    "# test_data = image_label_ds.take(test_count)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_data = train_image_label_ds.shuffle(buffer_size=train_count).repeat(-1)\n",
    "train_data = train_data.batch(BATCH_SIZE)\n",
    "train_data = train_data.prefetch(buffer_size=AUTOTUNE)\n",
    "\n",
    "#test_data = test_data.batch(BATCH_SIZE)\n",
    "print(train_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理验证数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "<PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.float32, name=None))>\n"
     ]
    }
   ],
   "source": [
    "#打乱顺序\n",
    "test_root = pathlib.Path(test_dir)\n",
    "test_image_paths = list(test_root.glob('*/*'))\n",
    "test_image_paths = [str(path) for path in test_image_paths]\n",
    "#random.shuffle(train_image_paths)\n",
    "#处理训练标签\n",
    "test_image_labels = [pathlib.Path(path).parent.name for path in test_image_paths]\n",
    "test_labels = [[label.split('_')[0]] for label in test_image_labels]\n",
    "new_test_labels=[]\n",
    "for n in test_labels:\n",
    "    new_test_labels.append(float(n[0]));  \n",
    "test_labels=new_test_labels\n",
    "print(test_labels)\n",
    "#处理训练图像\n",
    "test_path_ds = tf.data.Dataset.from_tensor_slices(test_image_paths)\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "test_image_ds = test_path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)\n",
    "test_label_ds = tf.data.Dataset.from_tensor_slices(test_labels)\n",
    "test_image_label_ds = tf.data.Dataset.zip((test_image_ds, test_label_ds))\n",
    "# image_count = len(all_image_paths)\n",
    "\n",
    "# test_count = int(image_count*0.2)\n",
    "test_count = len(test_image_paths)\n",
    "# train_data = image_label_ds.skip(test_count)\n",
    "# test_data = image_label_ds.take(test_count)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "test_data = test_image_label_ds.shuffle(buffer_size=test_count).repeat(-1)\n",
    "test_data = test_data.batch(BATCH_SIZE)\n",
    "test_data = test_data.prefetch(buffer_size=AUTOTUNE)\n",
    "\n",
    "#test_data = test_data.batch(BATCH_SIZE)\n",
    "print(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " efficientnetv2-l (Functiona  (None, 7, 7, 1280)       117746848 \n",
      " l)                                                              \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 1280)             0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " out_weight (Dense)          (None, 1)                 1281      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 117,748,129\n",
      "Trainable params: 117,235,553\n",
      "Non-trainable params: 512,576\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#要改\n",
    "EfficientNetV2L = tf.keras.applications.efficientnet_v2.EfficientNetV2L(weights='imagenet',include_top=False,input_shape=(224, 224, 3))\n",
    "#要改\n",
    "EfficientNetV2L.trainable = True\n",
    "inputs = tf.keras.Input(shape=(224, 224, 3))\n",
    "#要改\n",
    "x = EfficientNetV2L(inputs)\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "out_weight = tf.keras.layers.Dense(1,name='out_weight')(x)\n",
    "\n",
    "model = tf.keras.Model(inputs=inputs,\n",
    "                       outputs=[out_weight])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start time: Wed May  3 10:13:22 2023\n",
      "Epoch 1/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 1056.6342"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 543s 876ms/step - loss: 1056.6342 - val_loss: 428.2625\n",
      "Epoch 2/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 322.2961"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 525s 889ms/step - loss: 322.2961 - val_loss: 200.6970\n",
      "Epoch 3/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 140.4777"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 536s 907ms/step - loss: 140.4777 - val_loss: 126.5686\n",
      "Epoch 4/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 69.7611"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 558s 944ms/step - loss: 69.7611 - val_loss: 41.0801\n",
      "Epoch 5/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 46.1356"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 573s 971ms/step - loss: 46.1356 - val_loss: 22.3782\n",
      "Epoch 6/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 37.7356"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 589s 997ms/step - loss: 37.7356 - val_loss: 2.4515\n",
      "Epoch 7/300\n",
      "591/591 [==============================] - 365s 617ms/step - loss: 32.6258 - val_loss: 3.3901\n",
      "Epoch 8/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 32.3949"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 601s 1s/step - loss: 32.3949 - val_loss: 1.3842\n",
      "Epoch 9/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 32.1764"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 651s 1s/step - loss: 32.1764 - val_loss: 1.1799\n",
      "Epoch 10/300\n",
      "591/591 [==============================] - 431s 729ms/step - loss: 28.8980 - val_loss: 3.0317\n",
      "Epoch 11/300\n",
      "591/591 [==============================] - 409s 692ms/step - loss: 25.8948 - val_loss: 2.3694\n",
      "Epoch 12/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 18.8779"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 666s 1s/step - loss: 18.8779 - val_loss: 0.6920\n",
      "Epoch 13/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 14.8076"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 745s 1s/step - loss: 14.8076 - val_loss: 0.6580\n",
      "Epoch 14/300\n",
      "591/591 [==============================] - 509s 861ms/step - loss: 12.5155 - val_loss: 1.5146\n",
      "Epoch 15/300\n",
      "591/591 [==============================] - 488s 826ms/step - loss: 9.9852 - val_loss: 2.4038\n",
      "Epoch 16/300\n",
      "591/591 [==============================] - 487s 823ms/step - loss: 8.6972 - val_loss: 8.2643\n",
      "Epoch 17/300\n",
      "591/591 [==============================] - 483s 818ms/step - loss: 8.5491 - val_loss: 3.7748\n",
      "Epoch 18/300\n",
      "591/591 [==============================] - 516s 873ms/step - loss: 6.3410 - val_loss: 4.5005\n",
      "Epoch 19/300\n",
      "591/591 [==============================] - 640s 1s/step - loss: 5.4927 - val_loss: 4.6306\n",
      "Epoch 20/300\n",
      "591/591 [==============================] - 504s 854ms/step - loss: 5.2010 - val_loss: 0.7315\n",
      "Epoch 21/300\n",
      "591/591 [==============================] - 509s 861ms/step - loss: 5.3230 - val_loss: 2.3459\n",
      "Epoch 22/300\n",
      "591/591 [==============================] - 610s 1s/step - loss: 5.1463 - val_loss: 2.4400\n",
      "Epoch 23/300\n",
      "591/591 [==============================] - 645s 1s/step - loss: 3.9881 - val_loss: 2.3879\n",
      "Epoch 24/300\n",
      "591/591 [==============================] - 643s 1s/step - loss: 3.2696 - val_loss: 2.6260\n",
      "Epoch 25/300\n",
      "591/591 [==============================] - 643s 1s/step - loss: 3.3480 - val_loss: 0.8662\n",
      "Epoch 26/300\n",
      "591/591 [==============================] - 643s 1s/step - loss: 3.1167 - val_loss: 4.8763\n",
      "Epoch 27/300\n",
      "591/591 [==============================] - 651s 1s/step - loss: 3.5038 - val_loss: 2.7995\n",
      "Epoch 28/300\n",
      "591/591 [==============================] - 658s 1s/step - loss: 2.5024 - val_loss: 2.4645\n",
      "Epoch 29/300\n",
      "591/591 [==============================] - 649s 1s/step - loss: 3.4986 - val_loss: 0.9970\n",
      "Epoch 30/300\n",
      "591/591 [==============================] - 667s 1s/step - loss: 2.1829 - val_loss: 2.6248\n",
      "Epoch 31/300\n",
      "591/591 [==============================] - 668s 1s/step - loss: 1.9346 - val_loss: 2.8800\n",
      "Epoch 32/300\n",
      "591/591 [==============================] - 669s 1s/step - loss: 1.7053 - val_loss: 2.1930\n",
      "Epoch 33/300\n",
      "591/591 [==============================] - 652s 1s/step - loss: 2.0491 - val_loss: 1.0653\n",
      "Epoch 34/300\n",
      "591/591 [==============================] - 642s 1s/step - loss: 1.6850 - val_loss: 4.1422\n",
      "Epoch 35/300\n",
      "591/591 [==============================] - 653s 1s/step - loss: 1.9982 - val_loss: 2.6164\n",
      "Epoch 36/300\n",
      "591/591 [==============================] - 661s 1s/step - loss: 1.8182 - val_loss: 2.8905\n",
      "Epoch 37/300\n",
      "591/591 [==============================] - 650s 1s/step - loss: 1.6546 - val_loss: 5.5308\n",
      "Epoch 38/300\n",
      "591/591 [==============================] - 652s 1s/step - loss: 1.8719 - val_loss: 1.8137\n",
      "Epoch 39/300\n",
      "591/591 [==============================] - 661s 1s/step - loss: 1.4258 - val_loss: 4.6551\n",
      "Epoch 40/300\n",
      "591/591 [==============================] - 709s 1s/step - loss: 1.4168 - val_loss: 2.6949\n",
      "Epoch 41/300\n",
      "591/591 [==============================] - 696s 1s/step - loss: 1.7162 - val_loss: 2.7598\n",
      "Epoch 42/300\n",
      "591/591 [==============================] - 667s 1s/step - loss: 1.4733 - val_loss: 1.5115\n",
      "Epoch 43/300\n",
      "591/591 [==============================] - 554s 938ms/step - loss: 1.8029 - val_loss: 2.2448\n",
      "Epoch 44/300\n",
      "591/591 [==============================] - 507s 858ms/step - loss: 1.2117 - val_loss: 7.0002\n",
      "Epoch 45/300\n",
      "591/591 [==============================] - 504s 853ms/step - loss: 1.0702 - val_loss: 3.4722\n",
      "Epoch 46/300\n",
      "591/591 [==============================] - 500s 846ms/step - loss: 1.2225 - val_loss: 3.9937\n",
      "Epoch 47/300\n",
      "591/591 [==============================] - 487s 824ms/step - loss: 1.4459 - val_loss: 2.9560\n",
      "Epoch 48/300\n",
      "591/591 [==============================] - 486s 823ms/step - loss: 1.2262 - val_loss: 5.6743\n",
      "Epoch 49/300\n",
      "591/591 [==============================] - 485s 821ms/step - loss: 1.0280 - val_loss: 2.7661\n",
      "Epoch 50/300\n",
      "591/591 [==============================] - 485s 820ms/step - loss: 1.3696 - val_loss: 4.4411\n",
      "Epoch 51/300\n",
      "591/591 [==============================] - 485s 821ms/step - loss: 1.3242 - val_loss: 3.3887\n",
      "Epoch 52/300\n",
      "591/591 [==============================] - 485s 821ms/step - loss: 0.7685 - val_loss: 0.8760\n",
      "Epoch 53/300\n",
      "591/591 [==============================] - 486s 822ms/step - loss: 0.8514 - val_loss: 1.5371\n",
      "Epoch 54/300\n",
      "591/591 [==============================] - 484s 820ms/step - loss: 1.2688 - val_loss: 1.7275\n",
      "Epoch 55/300\n",
      "591/591 [==============================] - 487s 823ms/step - loss: 1.0065 - val_loss: 3.4416\n",
      "Epoch 56/300\n",
      "591/591 [==============================] - 504s 853ms/step - loss: 0.8762 - val_loss: 0.7219\n",
      "Epoch 57/300\n",
      "591/591 [==============================] - 519s 878ms/step - loss: 1.0256 - val_loss: 3.2444\n",
      "Epoch 58/300\n",
      "591/591 [==============================] - 531s 899ms/step - loss: 1.2987 - val_loss: 2.3401\n",
      "Epoch 59/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.8935 - val_loss: 2.0714\n",
      "Epoch 60/300\n",
      "591/591 [==============================] - 642s 1s/step - loss: 1.1649 - val_loss: 4.8079\n",
      "Epoch 61/300\n",
      "591/591 [==============================] - 653s 1s/step - loss: 0.9879 - val_loss: 3.4700\n",
      "Epoch 62/300\n",
      "591/591 [==============================] - 678s 1s/step - loss: 1.1297 - val_loss: 2.6368\n",
      "Epoch 63/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 0.9860"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 791s 1s/step - loss: 0.9860 - val_loss: 0.6511\n",
      "Epoch 64/300\n",
      "591/591 [==============================] - 601s 1s/step - loss: 0.8167 - val_loss: 1.7233\n",
      "Epoch 65/300\n",
      "591/591 [==============================] - 594s 1s/step - loss: 0.7793 - val_loss: 3.0162\n",
      "Epoch 66/300\n",
      "591/591 [==============================] - 590s 998ms/step - loss: 0.7359 - val_loss: 6.8929\n",
      "Epoch 67/300\n",
      "591/591 [==============================] - 586s 991ms/step - loss: 0.6732 - val_loss: 2.6705\n",
      "Epoch 68/300\n",
      "591/591 [==============================] - 582s 985ms/step - loss: 0.9392 - val_loss: 2.2846\n",
      "Epoch 69/300\n",
      "591/591 [==============================] - 580s 982ms/step - loss: 0.6885 - val_loss: 1.3745\n",
      "Epoch 70/300\n",
      "591/591 [==============================] - 579s 979ms/step - loss: 0.7202 - val_loss: 2.3748\n",
      "Epoch 71/300\n",
      "591/591 [==============================] - 580s 981ms/step - loss: 0.7131 - val_loss: 4.8071\n",
      "Epoch 72/300\n",
      "591/591 [==============================] - 580s 982ms/step - loss: 0.5144 - val_loss: 3.2631\n",
      "Epoch 73/300\n",
      "591/591 [==============================] - 580s 982ms/step - loss: 0.9399 - val_loss: 3.9127\n",
      "Epoch 74/300\n",
      "591/591 [==============================] - 580s 981ms/step - loss: 1.1093 - val_loss: 6.5596\n",
      "Epoch 75/300\n",
      "591/591 [==============================] - 579s 979ms/step - loss: 1.2036 - val_loss: 1.4345\n",
      "Epoch 76/300\n",
      "591/591 [==============================] - 578s 978ms/step - loss: 0.6919 - val_loss: 2.1056\n",
      "Epoch 77/300\n",
      "591/591 [==============================] - 579s 981ms/step - loss: 0.7372 - val_loss: 2.8392\n",
      "Epoch 78/300\n",
      "591/591 [==============================] - 580s 981ms/step - loss: 0.4987 - val_loss: 1.3849\n",
      "Epoch 79/300\n",
      "591/591 [==============================] - 579s 980ms/step - loss: 0.5284 - val_loss: 1.6663\n",
      "Epoch 80/300\n",
      "591/591 [==============================] - 578s 978ms/step - loss: 0.6944 - val_loss: 1.1150\n",
      "Epoch 81/300\n",
      "591/591 [==============================] - 577s 976ms/step - loss: 0.7488 - val_loss: 6.5486\n",
      "Epoch 82/300\n",
      "591/591 [==============================] - 578s 978ms/step - loss: 0.5113 - val_loss: 4.6247\n",
      "Epoch 83/300\n",
      "591/591 [==============================] - 579s 980ms/step - loss: 0.6093 - val_loss: 3.1459\n",
      "Epoch 84/300\n",
      "591/591 [==============================] - 578s 979ms/step - loss: 0.5587 - val_loss: 1.0601\n",
      "Epoch 85/300\n",
      "591/591 [==============================] - 581s 982ms/step - loss: 0.5839 - val_loss: 1.4518\n",
      "Epoch 86/300\n",
      "591/591 [==============================] - 578s 979ms/step - loss: 1.0450 - val_loss: 2.4093\n",
      "Epoch 87/300\n",
      "591/591 [==============================] - 578s 979ms/step - loss: 0.5959 - val_loss: 0.9353\n",
      "Epoch 88/300\n",
      "591/591 [==============================] - 580s 981ms/step - loss: 0.7834 - val_loss: 0.7501\n",
      "Epoch 89/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 0.6913"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 842s 1s/step - loss: 0.6913 - val_loss: 0.5780\n",
      "Epoch 90/300\n",
      "591/591 [==============================] - 617s 1s/step - loss: 0.6818 - val_loss: 1.5366\n",
      "Epoch 91/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 0.5101"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 878s 1s/step - loss: 0.5101 - val_loss: 0.5297\n",
      "Epoch 92/300\n",
      "591/591 [==============================] - 621s 1s/step - loss: 0.6308 - val_loss: 4.7831\n",
      "Epoch 93/300\n",
      "591/591 [==============================] - 617s 1s/step - loss: 0.6879 - val_loss: 1.9344\n",
      "Epoch 94/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 0.7589"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 877s 1s/step - loss: 0.7589 - val_loss: 0.4981\n",
      "Epoch 95/300\n",
      "591/591 [==============================] - 674s 1s/step - loss: 0.5819 - val_loss: 0.6852\n",
      "Epoch 96/300\n",
      "591/591 [==============================] - 666s 1s/step - loss: 0.6925 - val_loss: 3.0389\n",
      "Epoch 97/300\n",
      "591/591 [==============================] - 664s 1s/step - loss: 0.5448 - val_loss: 1.2847\n",
      "Epoch 98/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.7502 - val_loss: 4.6905\n",
      "Epoch 99/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.6705 - val_loss: 3.9375\n",
      "Epoch 100/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.5023 - val_loss: 2.8582\n",
      "Epoch 101/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.6357 - val_loss: 2.3864\n",
      "Epoch 102/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.6011 - val_loss: 1.6386\n",
      "Epoch 103/300\n",
      "591/591 [==============================] - 661s 1s/step - loss: 0.3477 - val_loss: 2.6670\n",
      "Epoch 104/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.6439 - val_loss: 1.2714\n",
      "Epoch 105/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.7654 - val_loss: 5.2471\n",
      "Epoch 106/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.3843 - val_loss: 1.9003\n",
      "Epoch 107/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.5460 - val_loss: 3.3881\n",
      "Epoch 108/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.3845 - val_loss: 1.2765\n",
      "Epoch 109/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.6114 - val_loss: 2.5013\n",
      "Epoch 110/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.3821 - val_loss: 1.9282\n",
      "Epoch 111/300\n",
      "591/591 [==============================] - 661s 1s/step - loss: 0.4258 - val_loss: 0.9670\n",
      "Epoch 112/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.6115 - val_loss: 1.7333\n",
      "Epoch 113/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.7701 - val_loss: 2.6011\n",
      "Epoch 114/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.3749 - val_loss: 1.4796\n",
      "Epoch 115/300\n",
      "591/591 [==============================] - 660s 1s/step - loss: 0.3234 - val_loss: 0.9522\n",
      "Epoch 116/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.3919 - val_loss: 0.6577\n",
      "Epoch 117/300\n",
      "591/591 [==============================] - 661s 1s/step - loss: 0.4972 - val_loss: 3.3817\n",
      "Epoch 118/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.6197 - val_loss: 5.2409\n",
      "Epoch 119/300\n",
      "591/591 [==============================] - 660s 1s/step - loss: 0.4406 - val_loss: 1.9411\n",
      "Epoch 120/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.4666 - val_loss: 2.1703\n",
      "Epoch 121/300\n",
      "591/591 [==============================] - 661s 1s/step - loss: 0.4749 - val_loss: 1.5593\n",
      "Epoch 122/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.3720 - val_loss: 1.3471\n",
      "Epoch 123/300\n",
      "591/591 [==============================] - 662s 1s/step - loss: 0.4298 - val_loss: 1.7810\n",
      "Epoch 124/300\n",
      "591/591 [==============================] - 663s 1s/step - loss: 0.5496 - val_loss: 2.4182\n",
      "Epoch 125/300\n",
      "591/591 [==============================] - 665s 1s/step - loss: 0.7187 - val_loss: 1.4943\n",
      "Epoch 126/300\n",
      "591/591 [==============================] - 667s 1s/step - loss: 0.4342 - val_loss: 1.9867\n",
      "Epoch 127/300\n",
      "591/591 [==============================] - 670s 1s/step - loss: 0.3999 - val_loss: 4.1520\n",
      "Epoch 128/300\n",
      "591/591 [==============================] - 670s 1s/step - loss: 0.4857 - val_loss: 1.6227\n",
      "Epoch 129/300\n",
      "591/591 [==============================] - 672s 1s/step - loss: 0.5147 - val_loss: 6.0898\n",
      "Epoch 130/300\n",
      "591/591 [==============================] - 672s 1s/step - loss: 0.3634 - val_loss: 0.6653\n",
      "Epoch 131/300\n",
      "591/591 [==============================] - 673s 1s/step - loss: 0.4375 - val_loss: 2.1656\n",
      "Epoch 132/300\n",
      "591/591 [==============================] - 674s 1s/step - loss: 0.4121 - val_loss: 0.9266\n",
      "Epoch 133/300\n",
      "591/591 [==============================] - 675s 1s/step - loss: 0.2470 - val_loss: 0.7878\n",
      "Epoch 134/300\n",
      "591/591 [==============================] - 677s 1s/step - loss: 0.5204 - val_loss: 3.4699\n",
      "Epoch 135/300\n",
      "591/591 [==============================] - 686s 1s/step - loss: 0.4719 - val_loss: 1.3002\n",
      "Epoch 136/300\n",
      "591/591 [==============================] - 701s 1s/step - loss: 0.2687 - val_loss: 1.2161\n",
      "Epoch 137/300\n",
      "591/591 [==============================] - 690s 1s/step - loss: 0.5443 - val_loss: 1.3845\n",
      "Epoch 138/300\n",
      "591/591 [==============================] - 833s 1s/step - loss: 0.8543 - val_loss: 0.5975\n",
      "Epoch 139/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 0.3563"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 1113s 2s/step - loss: 0.3563 - val_loss: 0.4687\n",
      "Epoch 140/300\n",
      "591/591 [==============================] - 887s 2s/step - loss: 0.4843 - val_loss: 4.7149\n",
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      "591/591 [==============================] - 907s 2s/step - loss: 0.3108 - val_loss: 2.6685\n",
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      "591/591 [==============================] - 897s 2s/step - loss: 0.4795 - val_loss: 1.0220\n",
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      "591/591 [==============================] - 874s 1s/step - loss: 0.7255 - val_loss: 13.6366\n",
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      "591/591 [==============================] - 737s 1s/step - loss: 0.3479 - val_loss: 0.5662\n",
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      "591/591 [==============================] - 730s 1s/step - loss: 0.4823 - val_loss: 3.4669\n",
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      "591/591 [==============================] - 730s 1s/step - loss: 0.2565 - val_loss: 0.5239\n",
      "Epoch 148/300\n",
      "591/591 [==============================] - 730s 1s/step - loss: 0.4891 - val_loss: 3.7151\n",
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      "591/591 [==============================] - 721s 1s/step - loss: 0.5254 - val_loss: 1.6745\n",
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      "591/591 [==============================] - 725s 1s/step - loss: 0.2561 - val_loss: 1.1525\n",
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      "591/591 [==============================] - 724s 1s/step - loss: 0.2122 - val_loss: 4.2148\n",
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      "591/591 [==============================] - 726s 1s/step - loss: 0.4289 - val_loss: 1.0831\n",
      "Epoch 153/300\n",
      "591/591 [==============================] - 732s 1s/step - loss: 0.4542 - val_loss: 3.9410\n",
      "Epoch 154/300\n",
      "591/591 [==============================] - 728s 1s/step - loss: 0.4787 - val_loss: 1.4073\n",
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      "591/591 [==============================] - 726s 1s/step - loss: 0.3191 - val_loss: 2.2244\n",
      "Epoch 156/300\n",
      "591/591 [==============================] - 728s 1s/step - loss: 0.5332 - val_loss: 1.8066\n",
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      "591/591 [==============================] - 731s 1s/step - loss: 0.4587 - val_loss: 2.4027\n",
      "Epoch 158/300\n",
      "591/591 [==============================] - 727s 1s/step - loss: 0.2855 - val_loss: 1.9790\n",
      "Epoch 159/300\n",
      "591/591 [==============================] - 719s 1s/step - loss: 0.4257 - val_loss: 3.2353\n",
      "Epoch 160/300\n",
      "591/591 [==============================] - 722s 1s/step - loss: 0.4361 - val_loss: 1.1745\n",
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      "591/591 [==============================] - 726s 1s/step - loss: 0.4505 - val_loss: 0.5765\n",
      "Epoch 162/300\n",
      "591/591 [==============================] - 745s 1s/step - loss: 0.3280 - val_loss: 0.8969\n",
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      "591/591 [==============================] - 754s 1s/step - loss: 0.3972 - val_loss: 3.1979\n",
      "Epoch 164/300\n",
      "591/591 [==============================] - 855s 1s/step - loss: 0.2458 - val_loss: 0.9496\n",
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      "591/591 [==============================] - 900s 2s/step - loss: 0.2479 - val_loss: 1.1427\n",
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      "591/591 [==============================] - 821s 1s/step - loss: 0.4488 - val_loss: 1.9104\n",
      "Epoch 167/300\n",
      "591/591 [==============================] - 734s 1s/step - loss: 0.5565 - val_loss: 1.6036\n",
      "Epoch 168/300\n",
      "591/591 [==============================] - 733s 1s/step - loss: 0.3875 - val_loss: 0.7162\n",
      "Epoch 169/300\n",
      "591/591 [==============================] - 741s 1s/step - loss: 0.5017 - val_loss: 0.6976\n",
      "Epoch 170/300\n",
      "591/591 [==============================] - 738s 1s/step - loss: 0.2448 - val_loss: 2.0217\n",
      "Epoch 171/300\n",
      "591/591 [==============================] - 750s 1s/step - loss: 0.3384 - val_loss: 0.9253\n",
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      "591/591 [==============================] - 754s 1s/step - loss: 0.3551 - val_loss: 3.8100\n",
      "Epoch 173/300\n",
      "591/591 [==============================] - 763s 1s/step - loss: 0.3097 - val_loss: 1.5146\n",
      "Epoch 174/300\n",
      "591/591 [==============================] - 907s 2s/step - loss: 0.4012 - val_loss: 5.8115\n",
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      "591/591 [==============================] - 918s 2s/step - loss: 0.2585 - val_loss: 1.4027\n",
      "Epoch 176/300\n",
      "591/591 [==============================] - 924s 2s/step - loss: 0.3954 - val_loss: 2.6801\n",
      "Epoch 177/300\n",
      "591/591 [==============================] - 954s 2s/step - loss: 0.3040 - val_loss: 1.7485\n",
      "Epoch 178/300\n",
      "591/591 [==============================] - 919s 2s/step - loss: 0.2582 - val_loss: 5.5442\n",
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      "591/591 [==============================] - 926s 2s/step - loss: 0.2386 - val_loss: 1.4531\n",
      "Epoch 180/300\n",
      "591/591 [==============================] - 941s 2s/step - loss: 0.6372 - val_loss: 0.9840\n",
      "Epoch 181/300\n",
      "591/591 [==============================] - 925s 2s/step - loss: 0.3260 - val_loss: 5.8746\n",
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      "591/591 [==============================] - 770s 1s/step - loss: 0.2889 - val_loss: 2.2537\n",
      "Epoch 183/300\n",
      "591/591 [==============================] - 775s 1s/step - loss: 0.2684 - val_loss: 0.7150\n",
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      "591/591 [==============================] - 813s 1s/step - loss: 0.2948 - val_loss: 1.6613\n",
      "Epoch 185/300\n",
      "591/591 [==============================] - 911s 2s/step - loss: 0.3148 - val_loss: 1.1532\n",
      "Epoch 186/300\n",
      "591/591 [==============================] - 918s 2s/step - loss: 0.2649 - val_loss: 0.5942\n",
      "Epoch 187/300\n",
      "591/591 [==============================] - 914s 2s/step - loss: 0.3885 - val_loss: 3.2128\n",
      "Epoch 188/300\n",
      "591/591 [==============================] - 920s 2s/step - loss: 0.4016 - val_loss: 1.4337\n",
      "Epoch 189/300\n",
      "591/591 [==============================] - 925s 2s/step - loss: 0.6199 - val_loss: 6.7751\n",
      "Epoch 190/300\n",
      "591/591 [==============================] - 880s 1s/step - loss: 0.4137 - val_loss: 1.2545\n",
      "Epoch 191/300\n",
      "591/591 [==============================] - 776s 1s/step - loss: 0.4933 - val_loss: 1.0617\n",
      "Epoch 192/300\n",
      "591/591 [==============================] - 731s 1s/step - loss: 0.2693 - val_loss: 0.7522\n",
      "Epoch 193/300\n",
      "591/591 [==============================] - 733s 1s/step - loss: 0.3338 - val_loss: 4.7512\n",
      "Epoch 194/300\n",
      "591/591 [==============================] - 733s 1s/step - loss: 0.6346 - val_loss: 1.8524\n",
      "Epoch 195/300\n",
      "591/591 [==============================] - 736s 1s/step - loss: 0.2476 - val_loss: 0.7147\n",
      "Epoch 196/300\n",
      "591/591 [==============================] - 767s 1s/step - loss: 0.2324 - val_loss: 0.5836\n",
      "Epoch 197/300\n",
      "591/591 [==============================] - 766s 1s/step - loss: 0.1615 - val_loss: 0.4859\n",
      "Epoch 198/300\n",
      "591/591 [==============================] - 768s 1s/step - loss: 0.3311 - val_loss: 0.8413\n",
      "Epoch 199/300\n",
      "591/591 [==============================] - 765s 1s/step - loss: 0.3382 - val_loss: 0.4919\n",
      "Epoch 200/300\n",
      "591/591 [==============================] - 769s 1s/step - loss: 0.3540 - val_loss: 0.9081\n",
      "Epoch 201/300\n",
      "591/591 [==============================] - 770s 1s/step - loss: 0.3569 - val_loss: 0.8301\n",
      "Epoch 202/300\n",
      "591/591 [==============================] - 768s 1s/step - loss: 0.3627 - val_loss: 3.3324\n",
      "Epoch 203/300\n",
      "591/591 [==============================] - 742s 1s/step - loss: 0.2540 - val_loss: 1.2081\n",
      "Epoch 204/300\n",
      "591/591 [==============================] - 742s 1s/step - loss: 0.2411 - val_loss: 2.6297\n",
      "Epoch 205/300\n",
      "591/591 [==============================] - 742s 1s/step - loss: 0.3803 - val_loss: 0.6904\n",
      "Epoch 206/300\n",
      "591/591 [==============================] - 737s 1s/step - loss: 0.3180 - val_loss: 1.6205\n",
      "Epoch 207/300\n",
      "591/591 [==============================] - 749s 1s/step - loss: 0.3212 - val_loss: 1.0594\n",
      "Epoch 208/300\n",
      "591/591 [==============================] - 773s 1s/step - loss: 0.2698 - val_loss: 2.1629\n",
      "Epoch 209/300\n",
      "591/591 [==============================] - 774s 1s/step - loss: 0.2338 - val_loss: 9.6974\n",
      "Epoch 210/300\n",
      "591/591 [==============================] - 774s 1s/step - loss: 0.4605 - val_loss: 1.1751\n",
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      "591/591 [==============================] - 772s 1s/step - loss: 0.4610 - val_loss: 1.1298\n",
      "Epoch 212/300\n",
      "591/591 [==============================] - 753s 1s/step - loss: 0.2584 - val_loss: 2.4028\n",
      "Epoch 213/300\n",
      "591/591 [==============================] - 737s 1s/step - loss: 0.2815 - val_loss: 1.1854\n",
      "Epoch 214/300\n",
      "591/591 [==============================] - 740s 1s/step - loss: 0.2762 - val_loss: 0.8000\n",
      "Epoch 215/300\n",
      "591/591 [==============================] - 741s 1s/step - loss: 0.2600 - val_loss: 0.9790\n",
      "Epoch 216/300\n",
      "591/591 [==============================] - 742s 1s/step - loss: 0.4612 - val_loss: 0.7294\n",
      "Epoch 217/300\n",
      "591/591 [==============================] - 763s 1s/step - loss: 0.4281 - val_loss: 1.6036\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 218/300\n",
      "591/591 [==============================] - 777s 1s/step - loss: 0.3402 - val_loss: 2.2068\n",
      "Epoch 219/300\n",
      "591/591 [==============================] - 777s 1s/step - loss: 0.2953 - val_loss: 1.3803\n",
      "Epoch 220/300\n",
      "591/591 [==============================] - 781s 1s/step - loss: 0.2070 - val_loss: 0.8149\n",
      "Epoch 221/300\n",
      "591/591 [==============================] - 775s 1s/step - loss: 0.2065 - val_loss: 0.9254\n",
      "Epoch 222/300\n",
      "591/591 [==============================] - 744s 1s/step - loss: 0.4169 - val_loss: 1.7400\n",
      "Epoch 223/300\n",
      "591/591 [==============================] - 743s 1s/step - loss: 0.3119 - val_loss: 1.0373\n",
      "Epoch 224/300\n",
      "591/591 [==============================] - 748s 1s/step - loss: 0.6624 - val_loss: 1.5890\n",
      "Epoch 225/300\n",
      "591/591 [==============================] - 751s 1s/step - loss: 0.3222 - val_loss: 0.9081\n",
      "Epoch 226/300\n",
      "591/591 [==============================] - 752s 1s/step - loss: 0.3027 - val_loss: 5.3075\n",
      "Epoch 227/300\n",
      "591/591 [==============================] - 776s 1s/step - loss: 0.2923 - val_loss: 3.7487\n",
      "Epoch 228/300\n",
      "591/591 [==============================] - 777s 1s/step - loss: 0.3573 - val_loss: 1.0708\n",
      "Epoch 229/300\n",
      "591/591 [==============================] - 778s 1s/step - loss: 0.2279 - val_loss: 1.1706\n",
      "Epoch 230/300\n",
      "591/591 [==============================] - 778s 1s/step - loss: 0.5206 - val_loss: 6.7208\n",
      "Epoch 231/300\n",
      "591/591 [==============================] - 759s 1s/step - loss: 0.2358 - val_loss: 1.1085\n",
      "Epoch 232/300\n",
      "591/591 [==============================] - 746s 1s/step - loss: 0.1864 - val_loss: 4.3473\n",
      "Epoch 233/300\n",
      "591/591 [==============================] - 745s 1s/step - loss: 0.3476 - val_loss: 1.1676\n",
      "Epoch 234/300\n",
      "591/591 [==============================] - 751s 1s/step - loss: 0.2045 - val_loss: 7.5990\n",
      "Epoch 235/300\n",
      "591/591 [==============================] - 746s 1s/step - loss: 0.3129 - val_loss: 1.5665\n",
      "Epoch 236/300\n",
      "591/591 [==============================] - 773s 1s/step - loss: 0.3638 - val_loss: 2.0426\n",
      "Epoch 237/300\n",
      "591/591 [==============================] - 780s 1s/step - loss: 0.1603 - val_loss: 0.4827\n",
      "Epoch 238/300\n",
      "591/591 [==============================] - 780s 1s/step - loss: 0.2408 - val_loss: 0.7850\n",
      "Epoch 239/300\n",
      "591/591 [==============================] - ETA: 0s - loss: 0.3389"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 339). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\24306\\Desktop\\EfficientNetV2L\\1\\EfficientNetV2L.ckpt\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591/591 [==============================] - 1059s 2s/step - loss: 0.3389 - val_loss: 0.3496\n",
      "Epoch 240/300\n",
      "591/591 [==============================] - 808s 1s/step - loss: 0.2704 - val_loss: 9.8191\n",
      "Epoch 241/300\n",
      "591/591 [==============================] - 789s 1s/step - loss: 0.3270 - val_loss: 0.8710\n",
      "Epoch 242/300\n",
      "591/591 [==============================] - 791s 1s/step - loss: 0.1723 - val_loss: 1.8307\n",
      "Epoch 243/300\n",
      "591/591 [==============================] - 791s 1s/step - loss: 0.2822 - val_loss: 1.2659\n",
      "Epoch 244/300\n",
      "591/591 [==============================] - 792s 1s/step - loss: 0.2602 - val_loss: 1.6760\n",
      "Epoch 245/300\n",
      "591/591 [==============================] - 820s 1s/step - loss: 0.3596 - val_loss: 1.1454\n",
      "Epoch 246/300\n",
      "591/591 [==============================] - 817s 1s/step - loss: 0.2087 - val_loss: 2.4870\n",
      "Epoch 247/300\n",
      "591/591 [==============================] - 818s 1s/step - loss: 0.1808 - val_loss: 0.6694\n",
      "Epoch 248/300\n",
      "591/591 [==============================] - 817s 1s/step - loss: 0.2995 - val_loss: 1.5235\n",
      "Epoch 249/300\n",
      "591/591 [==============================] - 801s 1s/step - loss: 0.1989 - val_loss: 1.1158\n",
      "Epoch 250/300\n",
      "591/591 [==============================] - 790s 1s/step - loss: 0.2565 - val_loss: 5.0173\n",
      "Epoch 251/300\n",
      "591/591 [==============================] - 789s 1s/step - loss: 0.3952 - val_loss: 1.7067\n",
      "Epoch 252/300\n",
      "591/591 [==============================] - 793s 1s/step - loss: 0.2941 - val_loss: 1.7669\n",
      "Epoch 253/300\n",
      "591/591 [==============================] - 788s 1s/step - loss: 0.2920 - val_loss: 3.9511\n",
      "Epoch 254/300\n",
      "591/591 [==============================] - 822s 1s/step - loss: 0.2474 - val_loss: 0.8026\n",
      "Epoch 255/300\n",
      "591/591 [==============================] - 826s 1s/step - loss: 0.3783 - val_loss: 0.7872\n",
      "Epoch 256/300\n",
      "591/591 [==============================] - 825s 1s/step - loss: 0.3950 - val_loss: 2.5120\n",
      "Epoch 257/300\n",
      "591/591 [==============================] - 820s 1s/step - loss: 0.1416 - val_loss: 0.6073\n",
      "Epoch 258/300\n",
      "591/591 [==============================] - 798s 1s/step - loss: 0.3638 - val_loss: 0.5807\n",
      "Epoch 259/300\n",
      "591/591 [==============================] - 784s 1s/step - loss: 0.2475 - val_loss: 0.6394\n",
      "Epoch 260/300\n",
      "591/591 [==============================] - 797s 1s/step - loss: 0.2219 - val_loss: 0.4525\n",
      "Epoch 261/300\n",
      "591/591 [==============================] - 791s 1s/step - loss: 0.2498 - val_loss: 0.7365\n",
      "Epoch 262/300\n",
      "591/591 [==============================] - 800s 1s/step - loss: 0.2614 - val_loss: 2.3120\n",
      "Epoch 263/300\n",
      "591/591 [==============================] - 830s 1s/step - loss: 0.1602 - val_loss: 1.9824\n",
      "Epoch 264/300\n",
      "591/591 [==============================] - 828s 1s/step - loss: 0.2298 - val_loss: 0.7408\n",
      "Epoch 265/300\n",
      "591/591 [==============================] - 832s 1s/step - loss: 0.2430 - val_loss: 6.0319\n",
      "Epoch 266/300\n",
      "591/591 [==============================] - 830s 1s/step - loss: 0.3077 - val_loss: 0.7987\n",
      "Epoch 267/300\n",
      "591/591 [==============================] - 813s 1s/step - loss: 0.3266 - val_loss: 1.0477\n",
      "Epoch 268/300\n",
      "591/591 [==============================] - 811s 1s/step - loss: 0.2022 - val_loss: 3.4419\n",
      "Epoch 269/300\n",
      "591/591 [==============================] - 810s 1s/step - loss: 0.2842 - val_loss: 0.8245\n",
      "Epoch 270/300\n",
      "591/591 [==============================] - 842s 1s/step - loss: 0.2014 - val_loss: 1.1326\n",
      "Epoch 271/300\n",
      "591/591 [==============================] - 919s 2s/step - loss: 0.2427 - val_loss: 0.6629\n",
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      "591/591 [==============================] - 985s 2s/step - loss: 0.3260 - val_loss: 0.6460\n",
      "Epoch 273/300\n",
      "591/591 [==============================] - 959s 2s/step - loss: 0.3751 - val_loss: 1.9957\n",
      "Epoch 274/300\n",
      "591/591 [==============================] - 962s 2s/step - loss: 0.3422 - val_loss: 1.2153\n",
      "Epoch 275/300\n",
      "591/591 [==============================] - 990s 2s/step - loss: 0.2218 - val_loss: 6.1725\n",
      "Epoch 276/300\n",
      "591/591 [==============================] - 991s 2s/step - loss: 0.2540 - val_loss: 1.2400\n",
      "Epoch 277/300\n",
      "591/591 [==============================] - 995s 2s/step - loss: 0.1858 - val_loss: 1.1836\n",
      "Epoch 278/300\n",
      "591/591 [==============================] - 1032s 2s/step - loss: 0.1764 - val_loss: 0.6597\n",
      "Epoch 279/300\n",
      "591/591 [==============================] - 990s 2s/step - loss: 0.2229 - val_loss: 1.2077\n",
      "Epoch 280/300\n",
      "591/591 [==============================] - 995s 2s/step - loss: 0.3236 - val_loss: 4.6833\n",
      "Epoch 281/300\n",
      "591/591 [==============================] - 986s 2s/step - loss: 0.1511 - val_loss: 0.8083\n",
      "Epoch 282/300\n",
      "591/591 [==============================] - 962s 2s/step - loss: 0.2996 - val_loss: 1.2002\n",
      "Epoch 283/300\n",
      "591/591 [==============================] - 968s 2s/step - loss: 0.2527 - val_loss: 1.1701\n",
      "Epoch 284/300\n",
      "591/591 [==============================] - 984s 2s/step - loss: 0.2370 - val_loss: 2.7366\n",
      "Epoch 285/300\n",
      "591/591 [==============================] - 978s 2s/step - loss: 0.1701 - val_loss: 2.3262\n",
      "Epoch 286/300\n",
      "591/591 [==============================] - 982s 2s/step - loss: 0.2694 - val_loss: 1.0765\n",
      "Epoch 287/300\n",
      "591/591 [==============================] - 991s 2s/step - loss: 0.2616 - val_loss: 0.9760\n",
      "Epoch 288/300\n",
      "591/591 [==============================] - 999s 2s/step - loss: 0.1561 - val_loss: 0.7553\n",
      "Epoch 289/300\n",
      "591/591 [==============================] - 947s 2s/step - loss: 0.1575 - val_loss: 1.4774\n",
      "Epoch 290/300\n",
      "591/591 [==============================] - 989s 2s/step - loss: 0.1558 - val_loss: 0.9473\n",
      "Epoch 291/300\n",
      "591/591 [==============================] - 996s 2s/step - loss: 0.4045 - val_loss: 0.8606\n",
      "Epoch 292/300\n",
      "591/591 [==============================] - 1000s 2s/step - loss: 0.1619 - val_loss: 0.6004\n",
      "Epoch 293/300\n",
      "591/591 [==============================] - 1001s 2s/step - loss: 0.2635 - val_loss: 3.0056\n",
      "Epoch 294/300\n",
      "591/591 [==============================] - 1028s 2s/step - loss: 0.1540 - val_loss: 6.5862\n",
      "Epoch 295/300\n",
      "591/591 [==============================] - 1050s 2s/step - loss: 0.2793 - val_loss: 4.4014\n",
      "Epoch 296/300\n",
      "591/591 [==============================] - 1011s 2s/step - loss: 0.2212 - val_loss: 1.3664\n",
      "Epoch 297/300\n",
      "591/591 [==============================] - 1007s 2s/step - loss: 0.2451 - val_loss: 1.0330\n",
      "Epoch 298/300\n",
      "591/591 [==============================] - 829s 1s/step - loss: 0.1686 - val_loss: 7.1746\n",
      "Epoch 299/300\n",
      "591/591 [==============================] - 812s 1s/step - loss: 0.3826 - val_loss: 1.4215\n",
      "Epoch 300/300\n",
      "591/591 [==============================] - 805s 1s/step - loss: 0.1987 - val_loss: 0.4324\n",
      "end time: Fri May  5 23:06:11 2023\n"
     ]
    }
   ],
   "source": [
    "print ('start time:',time.asctime( time.localtime(time.time()) ))\n",
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, decay=0.01),loss='mse')\n",
    "\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,save_best_only=True,monitor='val_loss')\n",
    "history = model.fit(train_data,\n",
    "                    steps_per_epoch=train_count//BATCH_SIZE,\n",
    "                    epochs=300,\n",
    "                    validation_data=test_data,\n",
    "                    validation_steps=test_count//BATCH_SIZE,\n",
    "                   callbacks=[cp_callback])\n",
    "#要改\n",
    "np.savetxt('EfficientNetV2L_loss_1.txt',history.history['loss'])\n",
    "#要改\n",
    "np.savetxt('EfficientNetV2L_valloss_1.txt',history.history['val_loss'])\n",
    "print ('end time:',time.asctime( time.localtime(time.time()) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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