{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "id": "167cef25", "metadata": { "id": "167cef25" }, "source": [ "## Imports and Installs" ] }, { "cell_type": "code", "execution_count": 1, "id": "2f18dc3d", "metadata": { "id": "2f18dc3d" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "from plotly import express as px\n", "\n", "import numpy as np\n", "import tensorflow.data as tfd" ] }, { "cell_type": "markdown", "id": "d4c6acfc", "metadata": { "id": "d4c6acfc" }, "source": [ "## Utilities" ] }, { "cell_type": "code", "execution_count": 2, "id": "d49354d9", "metadata": { "id": "d49354d9" }, "outputs": [], "source": [ "def plot_series(time, series, start=0, end=None, labels=None, title=None):\n", " # Visualizes time series data\n", " # Args:\n", " # time (array of int) - 時間點, 長度為T\n", " # series (list of array of int) - 時間點對應的資料列表，列表內時間序列數量為D，\n", " # 每筆資料長度為T，若非為列表則轉為列表\n", " # start (int) - 開始的資料序(第幾筆)\n", " # end (int) - 結束繪製的資料序(第幾筆)\n", " # labels (list of strings)- 對於多時間序列或多維度的標註\n", " # title (string)- 圖片標題\n", "\n", " # 若資料只有一筆，則轉為list\n", " if type(series) != list:\n", " series = [series]\n", "\n", " if not end:\n", " end = len(series[0])\n", "\n", " if labels:\n", " # 設立dictionary, 讓plotly畫訊號線時可以標註label\n", " dictionary = {\"time\": time}\n", " for idx, l in enumerate(labels):\n", " # 截斷資料，保留想看的部分，並分段紀錄於dictionary中\n", " dictionary.update({l: series[idx][start:end]})\n", " # 畫訊號線\n", " fig = px.line(dictionary,\n", " x=\"time\",\n", " y=list(dictionary.keys())[1:],\n", " width=1000,\n", " height=400,\n", " title=title)\n", " else:\n", " # 畫訊號線\n", " fig = px.line(x=time, y=series, width=1000, height=400, title=title)\n", " fig.show()\n", "\n", "\n", "# 合成資料生成\n", "def trend(time, slope=0):\n", " # 產生合成水平直線資料，其長度與時間等長，直線趨勢與設定slope相同\n", " # Args:\n", " # time (array of int) - 時間點, 長度為T\n", " # slope (float) - 設定資料的傾斜程度與正負\n", " # Returns:\n", " # series (array of float) - 產出slope 與設定相同的一條線\n", "\n", " series = slope * time\n", "\n", " return series\n", "\n", "\n", "def seasonal_pattern(season_time, pattern_type='triangle'):\n", " # 產生某個特定pattern，\n", " # Args:\n", " # season_time (array of float) - 周期內的時間點, 長度為T\n", " # pattern_type (str) - 這邊提供triangle與cosine\n", " # Returns:\n", " # data_pattern (array of float) - 根據自訂函式產出特定的pattern\n", "\n", " # 用特定function生成pattern\n", " if pattern_type == 'triangle':\n", " data_pattern = np.where(season_time < 0.5,\n", " season_time*2,\n", " 2-season_time*2)\n", " if pattern_type == 'cosine':\n", " data_pattern = np.cos(season_time*np.pi*2)\n", "\n", " return data_pattern\n", "\n", "\n", "def seasonality(time, period, amplitude=1, phase=30, pattern_type='triangle'):\n", " # Repeats the same pattern at each period\n", " # Args:\n", " # time (array of int) - 時間點, 長度為T\n", " # period (int) - 週期長度，必小於T\n", " # amplitude (float) - 序列幅度大小\n", " # phase (int) - 相位，為遞移量，正的向左(提前)、負的向右(延後)\n", " # pattern_type (str) - 這邊提供triangle與cosine\n", " # Returns:\n", " # data_pattern (array of float) - 有指定周期、振幅、相位、pattern後的time series\n", "\n", " # 將時間依週期重置為0\n", " season_time = ((time + phase) % period) / period\n", "\n", " # 產生週期性訊號並乘上幅度\n", " data_pattern = amplitude * seasonal_pattern(season_time, pattern_type)\n", "\n", " return data_pattern\n", "\n", "\n", "def noise(time, noise_level=1, seed=None):\n", " # 合成雜訊，這邊用高斯雜訊，機率密度為常態分布\n", " # Args:\n", " # time (array of int) - 時間點, 長度為T\n", " # noise_level (float) - 雜訊大小\n", " # seed (int) - 同樣的seed可以重現同樣的雜訊\n", " # Returns:\n", " # noise (array of float) - 雜訊時間序列\n", "\n", " # 做一個基於某個seed的雜訊生成器\n", " rnd = np.random.RandomState(seed)\n", "\n", " # 生與time同長度的雜訊，並且乘上雜訊大小 (不乘的話，標準差是1)\n", " noise = rnd.randn(len(time)) * noise_level\n", "\n", " return noise\n", "\n", "\n", "def toy_generation(time=np.arange(4 * 365),\n", " bias=500.,\n", " slope=0.1,\n", " period=180,\n", " amplitude=40.,\n", " phase=30,\n", " pattern_type='triangle',\n", " noise_level=5.,\n", " seed=2022):\n", " signal_series = bias\\\n", " + trend(time, slope)\\\n", " + seasonality(time,\n", " period,\n", " amplitude,\n", " phase,\n", " pattern_type)\n", " noise_series = noise(time, noise_level, seed)\n", "\n", " series = signal_series+noise_series\n", " return series\n", "\n", "\n", "# Dataset\n", "def win_ar_ds(series, size, shift=1):\n", " # 輸出Window-wise Forcasting Dataset\n", " # Args:\n", " # series (array of float) - 時序資料, 長度為T\n", " # size (int) - Window大小\n", " # shift (int) - 每個window起始點間距\n", " # Returns:\n", " # (tf.data.Dataset(母類名稱，切確type為MapDataset)) -\n", " # - 一個一次生一個window的生成器\n", "\n", " ds = tfd.Dataset.from_tensor_slices(series)\n", " ds = ds.window(size=size+1, shift=1, drop_remainder=True)\n", " ds = ds.flat_map(lambda ds: ds.batch(size+1))\n", " return ds.map(lambda x: (x[:-1], x[-1:]))\n", "\n", "# 評估function\n", "def MAE(pred, gt):\n", " # 計算Mean Absolute Error\n", " # Args:\n", " # pred (array of float) - 預測資料\n", " # gt (array of float) - 答案資料\n", " # Returns:\n", " # 計算結果 (float)\n", " return abs(pred-gt).mean()\n", "\n", "\n", "def MSE(pred, gt):\n", " # 計算Mean Square Error\n", " # Args:\n", " # pred (array of float) - 預測資料\n", " # gt (array of float) - 答案資料\n", " # Returns:\n", " # 計算結果 (float)\n", " return pow(pred-gt, 2).mean()\n", "\n", "\n", "def R2(pred, gt):\n", " # 計算R square score\n", " # Args:\n", " # pred (array of float) - 預測資料\n", " # gt (array of float) - 答案資料\n", " # Returns:\n", " # 計算結果 (float)\n", " return 1-pow(pred-gt, 2).sum()/pow(gt-gt.mean(), 2).sum()" ] }, { "cell_type": "markdown", "id": "ba1f30f8", "metadata": { "id": "ba1f30f8" }, "source": [ "## Generate the Synthetic Data" ] }, { "cell_type": "code", "execution_count": 3, "id": "b6e0aeed", "metadata": { "id": "b6e0aeed", "outputId": "84247213-f560-4281-a48d-a7598cb0e082", "colab": { "base_uri": "https://localhost:8080/", "height": 417 } }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "def split(x, train_size):\n", " return x[..., :train_size], x[..., train_size:]\n", "\n", "\n", "# 先合成資料，還有作資料分割\n", "time = np.arange(4*365) # 定義時間點\n", "series_sample = toy_generation(time, pattern_type='cosine') # 這就是我們合成出來的資料\n", "\n", "time_train, time_test = split(time, 3*365)\n", "series_train, series_test = split(series_sample, 3*365)\n", "\n", "# 畫出生成的訓練資料\n", "plot_series(time_train,\n", " series_train,\n", " labels=['Training Data'])" ] }, { "cell_type": "code", "execution_count": 4, "id": "8076a831", "metadata": { "id": "8076a831", "outputId": "49227302-8b22-40ee-d7bd-bab8371f6425", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(32, 7) (32, 1)\n" ] } ], "source": [ "window_size = 7\n", "\n", "# 用資料predict資料的training set\n", "train_ds = win_ar_ds(series_train, size=window_size) # 切time series\n", "train_loader = train_ds.cache()\\\n", " .shuffle(1000).batch(32, drop_remainder=True).prefetch(-1)\n", "\n", "# 用資料predict資料的testing set\n", "test_ds = win_ar_ds(series_test, size=window_size) # 切time series\n", "test_loader = test_ds.batch(32).prefetch(-1)\n", "\n", "for x, y in train_loader:\n", " pass\n", "\n", "print(x.shape, y.shape)" ] }, { "cell_type": "markdown", "id": "f1ac7064", "metadata": { "id": "f1ac7064" }, "source": [ "## Build the Model" ] }, { "cell_type": "markdown", "id": "0f75dbb6", "metadata": { "id": "0f75dbb6" }, "source": [ "### Many-to-Many or Many-to-One\n", "\n", "下面的模型主要由 SimpleRNN layers 組成\n", "\n", "模型中堆疊了兩層 RNN，由於第一層 RNN 應該將每個 timestep 的 **output** 傳給第二層 RNN 作為 **input**\n", "\n", "因此第一層 RNN 的 `return_sequences` 參數應設為 **True**\n", "\n", "而第二層 RNN 會在 input 的最後一個時間點接上 Dense Layer 做出下個時間點的預測，因此 `return_sequences` 參數應設為 **False**\n", "\n", "#### `return_sequences` = True:\n", "\n", "\n", "\n", "#### `return_sequences` = False:\n", "\n", "" ] }, { "cell_type": "markdown", "id": "f15c8e01", "metadata": { "id": "f15c8e01" }, "source": [ "### Input Shape\n", "\n", "SimpleRNN 的輸入為包含 `[batch, timesteps, feature]` 的 3 維張量輸入\n", "\n", "原來的資料窗口需從 (32, 7) reshape 為 (32, 7, 1)。 這表示窗口中的 7 個數據點將映射到 RNN 的 7 個時間步長\n", "\n", "* **Reshape 可以在輸入進模型前進行**\n", "\n", "* **也可以使用 Lambda 層在模型本身內執行此操作:**
\n", " 下面定義了一個 *lambda* 函數，該函數在輸入的最後一個軸上添加一個維度
\n", " 如此可將送進 RNN 的 input_shape 由 `(32, 7)` 改變為 `(32, 7, 1)`" ] }, { "cell_type": "markdown", "id": "2ab5b905", "metadata": { "id": "2ab5b905" }, "source": [ "### Model Output\n", "SimpleRNN 默認使用 *tanh* 為激活函數，輸出範圍為 [-1,1]\n", "\n", "\n", "\n", "而 training data 的值卻落在 400 以上，在模型輸出前可使用另一個 Lambda() 層將輸出進行縮放 x100" ] }, { "cell_type": "code", "execution_count": 5, "id": "5df6c3ef", "metadata": { "id": "5df6c3ef" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras import models, layers, losses, optimizers" ] }, { "cell_type": "code", "execution_count": 6, "id": "f715b55a", "metadata": { "id": "f715b55a", "outputId": "3829808d-4781-4933-f6ad-d204bd107e3c", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " lambda (Lambda) (None, 7, 1) 0 \n", " \n", " simple_rnn (SimpleRNN) (None, 7, 40) 1680 \n", " \n", " simple_rnn_1 (SimpleRNN) (None, 40) 3240 \n", " \n", " dense (Dense) (None, 1) 41 \n", " \n", " lambda_1 (Lambda) (None, 1) 0 \n", " \n", "=================================================================\n", "Total params: 4,961\n", "Trainable params: 4,961\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# 建立模型\n", "model_tune = tf.keras.models.Sequential([\n", " tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n", " input_shape=[window_size]),\n", " tf.keras.layers.SimpleRNN(40, return_sequences=True),\n", " tf.keras.layers.SimpleRNN(40),\n", " tf.keras.layers.Dense(1),\n", " tf.keras.layers.Lambda(lambda x: x * 100.0)\n", "])\n", "\n", "# 給出模型的 summary\n", "model_tune.summary()" ] }, { "cell_type": "markdown", "id": "bfa26a21", "metadata": { "id": "bfa26a21" }, "source": [ "## Tune the Learning Rate\n", "\n", "在正式的模型訓練之前，可以先對 learning rate 做優化
\n", "設置一個 learning rate scheduler 使其隨著每一個 epoch 動態逐步調大
\n", "觀察模型訓練過程中 loss 對於相應 learning rate 的改變
\n", "之後正式的模型訓練可以設定讓 loss 下降較快的 learning rate 以提高模型收斂效率
\n", "\n", "此外，loss function 這裡使用 Huber Loss:\n", "\n", "$\n", "L_{\\delta}(y,f(x))=\\left\\{\\begin{array}{ll}\n", " \\frac{1}{2}(y-f(x))^2, & \\mbox{for $|y-f(x)|\\leq\\delta$} \\\\\n", " \\delta\\cdot(|y-f(x)|-\\frac{1}{2}\\delta), & \\mbox{otherwise.} \\\\\n", " \\end{array} \\right.\n", "$ \n", "\n", "\n", "Huber Loss 可以降低對 outlier data 的懲罰程度，也就是說在訓練時參數收斂的方向比較不會受到 outlier 產生的 loss 的影響" ] }, { "cell_type": "code", "execution_count": 7, "id": "c5af18dd", "metadata": { "scrolled": true, "id": "c5af18dd", "outputId": "36b54e00-4f05-4810-9807-8b6fad6dcb91", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/600\n", "34/34 [==============================] - 9s 62ms/step - loss: 539.6182 - lr: 1.0000e-08\n", "Epoch 2/600\n", "34/34 [==============================] - 1s 18ms/step - loss: 539.6011 - lr: 1.0292e-08\n", "Epoch 3/600\n", "34/34 [==============================] - 1s 24ms/step - loss: 539.5839 - lr: 1.0593e-08\n", "Epoch 4/600\n", "34/34 [==============================] - 1s 16ms/step - loss: 539.5668 - lr: 1.0902e-08\n", "Epoch 5/600\n", "34/34 [==============================] - 1s 16ms/step - loss: 539.5482 - lr: 1.1220e-08\n", "Epoch 6/600\n", "34/34 [==============================] - 1s 19ms/step - loss: 539.5280 - lr: 1.1548e-08\n", "Epoch 7/600\n", "34/34 [==============================] - 0s 10ms/step - loss: 539.5079 - lr: 1.1885e-08\n", "Epoch 8/600\n", "34/34 [==============================] - 0s 10ms/step - loss: 539.4877 - lr: 1.2232e-08\n", "Epoch 9/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 539.4673 - lr: 1.2589e-08\n", "Epoch 10/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 539.4468 - lr: 1.2957e-08\n", "Epoch 11/600\n", "34/34 [==============================] - 0s 10ms/step - loss: 539.4261 - lr: 1.3335e-08\n", "Epoch 12/600\n", "34/34 [==============================] - 0s 11ms/step - loss: 539.4048 - lr: 1.3725e-08\n", "Epoch 13/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.3834 - lr: 1.4125e-08\n", "Epoch 14/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.3619 - lr: 1.4538e-08\n", "Epoch 15/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.3375 - lr: 1.4962e-08\n", "Epoch 16/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 539.3101 - lr: 1.5399e-08\n", "Epoch 17/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.2827 - lr: 1.5849e-08\n", "Epoch 18/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 539.2549 - lr: 1.6312e-08\n", "Epoch 19/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.2271 - lr: 1.6788e-08\n", "Epoch 20/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.1985 - lr: 1.7278e-08\n", "Epoch 21/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.1697 - lr: 1.7783e-08\n", "Epoch 22/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.1409 - lr: 1.8302e-08\n", "Epoch 23/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.1108 - lr: 1.8836e-08\n", "Epoch 24/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.0790 - lr: 1.9387e-08\n", "Epoch 25/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 539.0471 - lr: 1.9953e-08\n", "Epoch 26/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 539.0147 - lr: 2.0535e-08\n", "Epoch 27/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 538.9819 - lr: 2.1135e-08\n", "Epoch 28/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 538.9489 - lr: 2.1752e-08\n", "Epoch 29/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 538.9110 - lr: 2.2387e-08\n", "Epoch 30/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 538.8681 - lr: 2.3041e-08\n", "Epoch 31/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 538.8249 - lr: 2.3714e-08\n", "Epoch 32/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 538.7812 - lr: 2.4406e-08\n", "Epoch 33/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.7369 - lr: 2.5119e-08\n", "Epoch 34/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 538.6927 - lr: 2.5852e-08\n", "Epoch 35/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.6467 - lr: 2.6607e-08\n", "Epoch 36/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.5992 - lr: 2.7384e-08\n", "Epoch 37/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.5512 - lr: 2.8184e-08\n", "Epoch 38/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.5025 - lr: 2.9007e-08\n", "Epoch 39/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.4536 - lr: 2.9854e-08\n", "Epoch 40/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.4047 - lr: 3.0726e-08\n", "Epoch 41/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.3555 - lr: 3.1623e-08\n", "Epoch 42/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.3057 - lr: 3.2546e-08\n", "Epoch 43/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.2553 - lr: 3.3497e-08\n", "Epoch 44/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.2032 - lr: 3.4475e-08\n", "Epoch 45/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.1493 - lr: 3.5481e-08\n", "Epoch 46/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.0949 - lr: 3.6517e-08\n", "Epoch 47/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 538.0353 - lr: 3.7584e-08\n", "Epoch 48/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.9706 - lr: 3.8681e-08\n", "Epoch 49/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.9053 - lr: 3.9811e-08\n", "Epoch 50/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.8391 - lr: 4.0973e-08\n", "Epoch 51/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.7709 - lr: 4.2170e-08\n", "Epoch 52/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.7012 - lr: 4.3401e-08\n", "Epoch 53/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.6304 - lr: 4.4668e-08\n", "Epoch 54/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.5565 - lr: 4.5973e-08\n", "Epoch 55/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.4796 - lr: 4.7315e-08\n", "Epoch 56/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.4008 - lr: 4.8697e-08\n", "Epoch 57/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.3201 - lr: 5.0119e-08\n", "Epoch 58/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.2388 - lr: 5.1582e-08\n", "Epoch 59/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.1519 - lr: 5.3088e-08\n", "Epoch 60/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 537.0594 - lr: 5.4639e-08\n", "Epoch 61/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 536.9650 - lr: 5.6234e-08\n", "Epoch 62/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 536.8685 - lr: 5.7876e-08\n", "Epoch 63/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 536.7712 - lr: 5.9566e-08\n", "Epoch 64/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 536.6733 - lr: 6.1306e-08\n", "Epoch 65/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 536.5747 - lr: 6.3096e-08\n", "Epoch 66/600\n", "34/34 [==============================] - 0s 4ms/step - loss: 536.4740 - lr: 6.4938e-08\n", "Epoch 67/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 536.3706 - lr: 6.6834e-08\n", "Epoch 68/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 536.2618 - lr: 6.8786e-08\n", "Epoch 69/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 536.1470 - lr: 7.0795e-08\n", "Epoch 70/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 536.0300 - lr: 7.2862e-08\n", "Epoch 71/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 535.9087 - lr: 7.4989e-08\n", "Epoch 72/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 535.7836 - lr: 7.7179e-08\n", "Epoch 73/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 535.6558 - lr: 7.9433e-08\n", "Epoch 74/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 535.5254 - lr: 8.1752e-08\n", "Epoch 75/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 535.3891 - lr: 8.4140e-08\n", "Epoch 76/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 535.2462 - lr: 8.6596e-08\n", "Epoch 77/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 535.1008 - lr: 8.9125e-08\n", "Epoch 78/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 534.9540 - lr: 9.1728e-08\n", "Epoch 79/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 534.8051 - lr: 9.4406e-08\n", "Epoch 80/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 534.6512 - lr: 9.7163e-08\n", "Epoch 81/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 534.4911 - lr: 1.0000e-07\n", "Epoch 82/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 534.3260 - lr: 1.0292e-07\n", "Epoch 83/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 534.1556 - lr: 1.0593e-07\n", "Epoch 84/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 533.9802 - lr: 1.0902e-07\n", "Epoch 85/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 533.7994 - lr: 1.1220e-07\n", "Epoch 86/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 533.6118 - lr: 1.1548e-07\n", "Epoch 87/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 533.4191 - lr: 1.1885e-07\n", "Epoch 88/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 533.2239 - lr: 1.2232e-07\n", "Epoch 89/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 533.0254 - lr: 1.2589e-07\n", "Epoch 90/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 532.8195 - lr: 1.2957e-07\n", "Epoch 91/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 532.6060 - lr: 1.3335e-07\n", "Epoch 92/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 532.3867 - lr: 1.3725e-07\n", "Epoch 93/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 532.1606 - lr: 1.4125e-07\n", "Epoch 94/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 531.9266 - lr: 1.4538e-07\n", "Epoch 95/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 531.6854 - lr: 1.4962e-07\n", "Epoch 96/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 531.4407 - lr: 1.5399e-07\n", "Epoch 97/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 531.1887 - lr: 1.5849e-07\n", "Epoch 98/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 530.9283 - lr: 1.6312e-07\n", "Epoch 99/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 530.6604 - lr: 1.6788e-07\n", "Epoch 100/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 530.3835 - lr: 1.7278e-07\n", "Epoch 101/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 530.0980 - lr: 1.7783e-07\n", "Epoch 102/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 529.8063 - lr: 1.8302e-07\n", "Epoch 103/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 529.5075 - lr: 1.8836e-07\n", "Epoch 104/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 529.1985 - lr: 1.9387e-07\n", "Epoch 105/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 528.8805 - lr: 1.9953e-07\n", "Epoch 106/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 528.5526 - lr: 2.0535e-07\n", "Epoch 107/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 528.2155 - lr: 2.1135e-07\n", "Epoch 108/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 527.8710 - lr: 2.1752e-07\n", "Epoch 109/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 527.5157 - lr: 2.2387e-07\n", "Epoch 110/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 527.1492 - lr: 2.3041e-07\n", "Epoch 111/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 526.7720 - lr: 2.3714e-07\n", "Epoch 112/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 526.3853 - lr: 2.4406e-07\n", "Epoch 113/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 525.9886 - lr: 2.5119e-07\n", "Epoch 114/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 525.5795 - lr: 2.5852e-07\n", "Epoch 115/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 525.1580 - lr: 2.6607e-07\n", "Epoch 116/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 524.7251 - lr: 2.7384e-07\n", "Epoch 117/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 524.2811 - lr: 2.8184e-07\n", "Epoch 118/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 523.8229 - lr: 2.9007e-07\n", "Epoch 119/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 523.3512 - lr: 2.9854e-07\n", "Epoch 120/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 522.8671 - lr: 3.0726e-07\n", "Epoch 121/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 522.3696 - lr: 3.1623e-07\n", "Epoch 122/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 521.8579 - lr: 3.2546e-07\n", "Epoch 123/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 521.3316 - lr: 3.3497e-07\n", "Epoch 124/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 520.7908 - lr: 3.4475e-07\n", "Epoch 125/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 520.2346 - lr: 3.5481e-07\n", "Epoch 126/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 519.6622 - lr: 3.6517e-07\n", "Epoch 127/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 519.0746 - lr: 3.7584e-07\n", "Epoch 128/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 518.4709 - lr: 3.8681e-07\n", "Epoch 129/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 517.8505 - lr: 3.9811e-07\n", "Epoch 130/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 517.2122 - lr: 4.0973e-07\n", "Epoch 131/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 516.5558 - lr: 4.2170e-07\n", "Epoch 132/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 515.8822 - lr: 4.3401e-07\n", "Epoch 133/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 515.1900 - lr: 4.4668e-07\n", "Epoch 134/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 514.4785 - lr: 4.5973e-07\n", "Epoch 135/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 513.7472 - lr: 4.7315e-07\n", "Epoch 136/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 512.9960 - lr: 4.8697e-07\n", "Epoch 137/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 512.2241 - lr: 5.0119e-07\n", "Epoch 138/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 511.4312 - lr: 5.1582e-07\n", "Epoch 139/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 510.6165 - lr: 5.3088e-07\n", "Epoch 140/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 509.7798 - lr: 5.4639e-07\n", "Epoch 141/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 508.9207 - lr: 5.6234e-07\n", "Epoch 142/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 508.0387 - lr: 5.7876e-07\n", "Epoch 143/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 507.1331 - lr: 5.9566e-07\n", "Epoch 144/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 506.2029 - lr: 6.1306e-07\n", "Epoch 145/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 505.2481 - lr: 6.3096e-07\n", "Epoch 146/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 504.2678 - lr: 6.4938e-07\n", "Epoch 147/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 503.2610 - lr: 6.6834e-07\n", "Epoch 148/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 502.2278 - lr: 6.8786e-07\n", "Epoch 149/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 501.1673 - lr: 7.0795e-07\n", "Epoch 150/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 500.0791 - lr: 7.2862e-07\n", "Epoch 151/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 498.9628 - lr: 7.4989e-07\n", "Epoch 152/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 497.8174 - lr: 7.7179e-07\n", "Epoch 153/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 496.6422 - lr: 7.9433e-07\n", "Epoch 154/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 495.4368 - lr: 8.1752e-07\n", "Epoch 155/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 494.2011 - lr: 8.4140e-07\n", "Epoch 156/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 492.9337 - lr: 8.6596e-07\n", "Epoch 157/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 491.6339 - lr: 8.9125e-07\n", "Epoch 158/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 490.3011 - lr: 9.1728e-07\n", "Epoch 159/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 488.9350 - lr: 9.4406e-07\n", "Epoch 160/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 487.5349 - lr: 9.7163e-07\n", "Epoch 161/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 486.0997 - lr: 1.0000e-06\n", "Epoch 162/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 484.6287 - lr: 1.0292e-06\n", "Epoch 163/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 483.1220 - lr: 1.0593e-06\n", "Epoch 164/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 481.5783 - lr: 1.0902e-06\n", "Epoch 165/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 479.9969 - lr: 1.1220e-06\n", "Epoch 166/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 478.3772 - lr: 1.1548e-06\n", "Epoch 167/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 476.7184 - lr: 1.1885e-06\n", "Epoch 168/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 475.0198 - lr: 1.2232e-06\n", "Epoch 169/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 473.2800 - lr: 1.2589e-06\n", "Epoch 170/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 471.4989 - lr: 1.2957e-06\n", "Epoch 171/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 469.6758 - lr: 1.3335e-06\n", "Epoch 172/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 467.8091 - lr: 1.3725e-06\n", "Epoch 173/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 465.8982 - lr: 1.4125e-06\n", "Epoch 174/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 463.9419 - lr: 1.4538e-06\n", "Epoch 175/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 461.9389 - lr: 1.4962e-06\n", "Epoch 176/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 459.8872 - lr: 1.5399e-06\n", "Epoch 177/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 457.7850 - lr: 1.5849e-06\n", "Epoch 178/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 455.6308 - lr: 1.6312e-06\n", "Epoch 179/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 453.4215 - lr: 1.6788e-06\n", "Epoch 180/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 451.1545 - lr: 1.7278e-06\n", "Epoch 181/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 448.8252 - lr: 1.7783e-06\n", "Epoch 182/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 446.4275 - lr: 1.8302e-06\n", "Epoch 183/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 443.9549 - lr: 1.8836e-06\n", "Epoch 184/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 441.3976 - lr: 1.9387e-06\n", "Epoch 185/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 438.7403 - lr: 1.9953e-06\n", "Epoch 186/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 435.9630 - lr: 2.0535e-06\n", "Epoch 187/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 433.0384 - lr: 2.1135e-06\n", "Epoch 188/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 429.9283 - lr: 2.1752e-06\n", "Epoch 189/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 426.5790 - lr: 2.2387e-06\n", "Epoch 190/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 422.9212 - lr: 2.3041e-06\n", "Epoch 191/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 418.8669 - lr: 2.3714e-06\n", "Epoch 192/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 414.3439 - lr: 2.4406e-06\n", "Epoch 193/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 409.3221 - lr: 2.5119e-06\n", "Epoch 194/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 403.9007 - lr: 2.5852e-06\n", "Epoch 195/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 398.3666 - lr: 2.6607e-06\n", "Epoch 196/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 393.0878 - lr: 2.7384e-06\n", "Epoch 197/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 388.3112 - lr: 2.8184e-06\n", "Epoch 198/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 384.0572 - lr: 2.9007e-06\n", "Epoch 199/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 380.2050 - lr: 2.9854e-06\n", "Epoch 200/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 376.6176 - lr: 3.0726e-06\n", "Epoch 201/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 373.1845 - lr: 3.1623e-06\n", "Epoch 202/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 369.8351 - lr: 3.2546e-06\n", "Epoch 203/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 366.5199 - lr: 3.3497e-06\n", "Epoch 204/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 363.2072 - lr: 3.4475e-06\n", "Epoch 205/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 359.8730 - lr: 3.5481e-06\n", "Epoch 206/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 356.4996 - lr: 3.6517e-06\n", "Epoch 207/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 353.0729 - lr: 3.7584e-06\n", "Epoch 208/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 349.5807 - lr: 3.8681e-06\n", "Epoch 209/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 346.0121 - lr: 3.9811e-06\n", "Epoch 210/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 342.3571 - lr: 4.0973e-06\n", "Epoch 211/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 338.6058 - lr: 4.2170e-06\n", "Epoch 212/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 334.7473 - lr: 4.3401e-06\n", "Epoch 213/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 330.7712 - lr: 4.4668e-06\n", "Epoch 214/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 326.6655 - lr: 4.5973e-06\n", "Epoch 215/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 322.4170 - lr: 4.7315e-06\n", "Epoch 216/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 318.0116 - lr: 4.8697e-06\n", "Epoch 217/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 313.4329 - lr: 5.0119e-06\n", "Epoch 218/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 308.6620 - lr: 5.1582e-06\n", "Epoch 219/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 303.6778 - lr: 5.3088e-06\n", "Epoch 220/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 298.4555 - lr: 5.4639e-06\n", "Epoch 221/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 292.9671 - lr: 5.6234e-06\n", "Epoch 222/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 287.1802 - lr: 5.7876e-06\n", "Epoch 223/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 281.0586 - lr: 5.9566e-06\n", "Epoch 224/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 274.5612 - lr: 6.1306e-06\n", "Epoch 225/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 267.6433 - lr: 6.3096e-06\n", "Epoch 226/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 260.2580 - lr: 6.4938e-06\n", "Epoch 227/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 252.3535 - lr: 6.6834e-06\n", "Epoch 228/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 243.8380 - lr: 6.8786e-06\n", "Epoch 229/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 234.2929 - lr: 7.0795e-06\n", "Epoch 230/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 221.6157 - lr: 7.2862e-06\n", "Epoch 231/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 203.4230 - lr: 7.4989e-06\n", "Epoch 232/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 184.2933 - lr: 7.7179e-06\n", "Epoch 233/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 163.4403 - lr: 7.9433e-06\n", "Epoch 234/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 139.8531 - lr: 8.1752e-06\n", "Epoch 235/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 115.1867 - lr: 8.4140e-06\n", "Epoch 236/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 92.2161 - lr: 8.6596e-06\n", "Epoch 237/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 73.9108 - lr: 8.9125e-06\n", "Epoch 238/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 61.3061 - lr: 9.1728e-06\n", "Epoch 239/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 53.2227 - lr: 9.4406e-06\n", "Epoch 240/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 47.6441 - lr: 9.7163e-06\n", "Epoch 241/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 43.8421 - lr: 1.0000e-05\n", "Epoch 242/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 41.1668 - lr: 1.0292e-05\n", "Epoch 243/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 39.3824 - lr: 1.0593e-05\n", "Epoch 244/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 38.1871 - lr: 1.0902e-05\n", "Epoch 245/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 37.3798 - lr: 1.1220e-05\n", "Epoch 246/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 36.8588 - lr: 1.1548e-05\n", "Epoch 247/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.5470 - lr: 1.1885e-05\n", "Epoch 248/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.3576 - lr: 1.2232e-05\n", "Epoch 249/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.2575 - lr: 1.2589e-05\n", "Epoch 250/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1973 - lr: 1.2957e-05\n", "Epoch 251/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1612 - lr: 1.3335e-05\n", "Epoch 252/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 36.1407 - lr: 1.3725e-05\n", "Epoch 253/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1358 - lr: 1.4125e-05\n", "Epoch 254/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1335 - lr: 1.4538e-05\n", "Epoch 255/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1342 - lr: 1.4962e-05\n", "Epoch 256/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1360 - lr: 1.5399e-05\n", "Epoch 257/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1274 - lr: 1.5849e-05\n", "Epoch 258/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1312 - lr: 1.6312e-05\n", "Epoch 259/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1261 - lr: 1.6788e-05\n", "Epoch 260/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1286 - lr: 1.7278e-05\n", "Epoch 261/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.1328 - lr: 1.7783e-05\n", "Epoch 262/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1276 - lr: 1.8302e-05\n", "Epoch 263/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1297 - lr: 1.8836e-05\n", "Epoch 264/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1243 - lr: 1.9387e-05\n", "Epoch 265/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1301 - lr: 1.9953e-05\n", "Epoch 266/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1277 - lr: 2.0535e-05\n", "Epoch 267/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.1302 - lr: 2.1135e-05\n", "Epoch 268/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1336 - lr: 2.1752e-05\n", "Epoch 269/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1205 - lr: 2.2387e-05\n", "Epoch 270/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1224 - lr: 2.3041e-05\n", "Epoch 271/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1317 - lr: 2.3714e-05\n", "Epoch 272/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1184 - lr: 2.4406e-05\n", "Epoch 273/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1455 - lr: 2.5119e-05\n", "Epoch 274/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1133 - lr: 2.5852e-05\n", "Epoch 275/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1254 - lr: 2.6607e-05\n", "Epoch 276/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1265 - lr: 2.7384e-05\n", "Epoch 277/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1230 - lr: 2.8184e-05\n", "Epoch 278/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1259 - lr: 2.9007e-05\n", "Epoch 279/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1196 - lr: 2.9854e-05\n", "Epoch 280/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1285 - lr: 3.0726e-05\n", "Epoch 281/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1077 - lr: 3.1623e-05\n", "Epoch 282/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1189 - lr: 3.2546e-05\n", "Epoch 283/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1134 - lr: 3.3497e-05\n", "Epoch 284/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1323 - lr: 3.4475e-05\n", "Epoch 285/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1231 - lr: 3.5481e-05\n", "Epoch 286/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1197 - lr: 3.6517e-05\n", "Epoch 287/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1169 - lr: 3.7584e-05\n", "Epoch 288/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1202 - lr: 3.8681e-05\n", "Epoch 289/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1389 - lr: 3.9811e-05\n", "Epoch 290/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 36.1053 - lr: 4.0973e-05\n", "Epoch 291/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1127 - lr: 4.2170e-05\n", "Epoch 292/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 36.1117 - lr: 4.3401e-05\n", "Epoch 293/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1264 - lr: 4.4668e-05\n", "Epoch 294/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1180 - lr: 4.5973e-05\n", "Epoch 295/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1099 - lr: 4.7315e-05\n", "Epoch 296/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.0974 - lr: 4.8697e-05\n", "Epoch 297/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.1130 - lr: 5.0119e-05\n", "Epoch 298/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.1352 - lr: 5.1582e-05\n", "Epoch 299/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1643 - lr: 5.3088e-05\n", "Epoch 300/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0950 - lr: 5.4639e-05\n", "Epoch 301/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1237 - lr: 5.6234e-05\n", "Epoch 302/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.0682 - lr: 5.7876e-05\n", "Epoch 303/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0878 - lr: 5.9566e-05\n", "Epoch 304/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1051 - lr: 6.1306e-05\n", "Epoch 305/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0978 - lr: 6.3096e-05\n", "Epoch 306/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.1084 - lr: 6.4938e-05\n", "Epoch 307/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0337 - lr: 6.6834e-05\n", "Epoch 308/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1018 - lr: 6.8786e-05\n", "Epoch 309/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1293 - lr: 7.0795e-05\n", "Epoch 310/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0217 - lr: 7.2862e-05\n", "Epoch 311/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0771 - lr: 7.4989e-05\n", "Epoch 312/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0013 - lr: 7.7179e-05\n", "Epoch 313/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.0026 - lr: 7.9433e-05\n", "Epoch 314/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0349 - lr: 8.1752e-05\n", "Epoch 315/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 35.9670 - lr: 8.4140e-05\n", "Epoch 316/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.1578 - lr: 8.6596e-05\n", "Epoch 317/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 36.2118 - lr: 8.9125e-05\n", "Epoch 318/600\n", "34/34 [==============================] - 0s 12ms/step - loss: 35.9881 - lr: 9.1728e-05\n", "Epoch 319/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 35.7920 - lr: 9.4406e-05\n", "Epoch 320/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 35.7758 - lr: 9.7163e-05\n", "Epoch 321/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 35.7274 - lr: 1.0000e-04\n", "Epoch 322/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 35.5649 - lr: 1.0292e-04\n", "Epoch 323/600\n", "34/34 [==============================] - 0s 13ms/step - loss: 35.4338 - lr: 1.0593e-04\n", "Epoch 324/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 35.7980 - lr: 1.0902e-04\n", "Epoch 325/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 35.7290 - lr: 1.1220e-04\n", "Epoch 326/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 35.3989 - lr: 1.1548e-04\n", "Epoch 327/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 34.8162 - lr: 1.1885e-04\n", "Epoch 328/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 35.0509 - lr: 1.2232e-04\n", "Epoch 329/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 34.2556 - lr: 1.2589e-04\n", "Epoch 330/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 33.7043 - lr: 1.2957e-04\n", "Epoch 331/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 33.1797 - lr: 1.3335e-04\n", "Epoch 332/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 32.8679 - lr: 1.3725e-04\n", "Epoch 333/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 32.5396 - lr: 1.4125e-04\n", "Epoch 334/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 30.9085 - lr: 1.4538e-04\n", "Epoch 335/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 30.6187 - lr: 1.4962e-04\n", "Epoch 336/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 28.5011 - lr: 1.5399e-04\n", "Epoch 337/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 26.4666 - lr: 1.5849e-04\n", "Epoch 338/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 23.9924 - lr: 1.6312e-04\n", "Epoch 339/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 19.6613 - lr: 1.6788e-04\n", "Epoch 340/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.4720 - lr: 1.7278e-04\n", "Epoch 341/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 10.5599 - lr: 1.7783e-04\n", "Epoch 342/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.7385 - lr: 1.8302e-04\n", "Epoch 343/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 8.7194 - lr: 1.8836e-04\n", "Epoch 344/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.4030 - lr: 1.9387e-04\n", "Epoch 345/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.9870 - lr: 1.9953e-04\n", "Epoch 346/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 8.0562 - lr: 2.0535e-04\n", "Epoch 347/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.9576 - lr: 2.1135e-04\n", "Epoch 348/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 6.8811 - lr: 2.1752e-04\n", "Epoch 349/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 6.2956 - lr: 2.2387e-04\n", "Epoch 350/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.3346 - lr: 2.3041e-04\n", "Epoch 351/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.9273 - lr: 2.3714e-04\n", "Epoch 352/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.6099 - lr: 2.4406e-04\n", "Epoch 353/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.2223 - lr: 2.5119e-04\n", "Epoch 354/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 7.4229 - lr: 2.5852e-04\n", "Epoch 355/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.4080 - lr: 2.6607e-04\n", "Epoch 356/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.8559 - lr: 2.7384e-04\n", "Epoch 357/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 8.0111 - lr: 2.8184e-04\n", "Epoch 358/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.9160 - lr: 2.9007e-04\n", "Epoch 359/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.6099 - lr: 2.9854e-04\n", "Epoch 360/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.2830 - lr: 3.0726e-04\n", "Epoch 361/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.5697 - lr: 3.1623e-04\n", "Epoch 362/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.6904 - lr: 3.2546e-04\n", "Epoch 363/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.7961 - lr: 3.3497e-04\n", "Epoch 364/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.9666 - lr: 3.4475e-04\n", "Epoch 365/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.7174 - lr: 3.5481e-04\n", "Epoch 366/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.2465 - lr: 3.6517e-04\n", "Epoch 367/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.4403 - lr: 3.7584e-04\n", "Epoch 368/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.9016 - lr: 3.8681e-04\n", "Epoch 369/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.5991 - lr: 3.9811e-04\n", "Epoch 370/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.1967 - lr: 4.0973e-04\n", "Epoch 371/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.2886 - lr: 4.2170e-04\n", "Epoch 372/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 6.4529 - lr: 4.3401e-04\n", "Epoch 373/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 6.5367 - lr: 4.4668e-04\n", "Epoch 374/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 7.6594 - lr: 4.5973e-04\n", "Epoch 375/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 7.6513 - lr: 4.7315e-04\n", "Epoch 376/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 7.2274 - lr: 4.8697e-04\n", "Epoch 377/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 7.3142 - lr: 5.0119e-04\n", "Epoch 378/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 5.8251 - lr: 5.1582e-04\n", "Epoch 379/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 5.5855 - lr: 5.3088e-04\n", "Epoch 380/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 6.4124 - lr: 5.4639e-04\n", "Epoch 381/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 6.8828 - lr: 5.6234e-04\n", "Epoch 382/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 7.4156 - lr: 5.7876e-04\n", "Epoch 383/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 8.4400 - lr: 5.9566e-04\n", "Epoch 384/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 8.0175 - lr: 6.1306e-04\n", "Epoch 385/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.9596 - lr: 6.3096e-04\n", "Epoch 386/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 7.8252 - lr: 6.4938e-04\n", "Epoch 387/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.7576 - lr: 6.6834e-04\n", "Epoch 388/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 7.7065 - lr: 6.8786e-04\n", "Epoch 389/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 8.6134 - lr: 7.0795e-04\n", "Epoch 390/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 8.1124 - lr: 7.2862e-04\n", "Epoch 391/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 7.8575 - lr: 7.4989e-04\n", "Epoch 392/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.4289 - lr: 7.7179e-04\n", "Epoch 393/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 11.7193 - lr: 7.9433e-04\n", "Epoch 394/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 13.9370 - lr: 8.1752e-04\n", "Epoch 395/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 8.7508 - lr: 8.4140e-04\n", "Epoch 396/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 8.5216 - lr: 8.6596e-04\n", "Epoch 397/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 7.6743 - lr: 8.9125e-04\n", "Epoch 398/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 7.3017 - lr: 9.1728e-04\n", "Epoch 399/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 8.1503 - lr: 9.4406e-04\n", "Epoch 400/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.9752 - lr: 9.7163e-04\n", "Epoch 401/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 7.5686 - lr: 0.0010\n", "Epoch 402/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 17.6623 - lr: 0.0010\n", "Epoch 403/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 10.5128 - lr: 0.0011\n", "Epoch 404/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 7.6479 - lr: 0.0011\n", "Epoch 405/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 9.7267 - lr: 0.0011\n", "Epoch 406/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 11.7152 - lr: 0.0012\n", "Epoch 407/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 17.8822 - lr: 0.0012\n", "Epoch 408/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.9209 - lr: 0.0012\n", "Epoch 409/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 10.7595 - lr: 0.0013\n", "Epoch 410/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 11.0750 - lr: 0.0013\n", "Epoch 411/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.6586 - lr: 0.0013\n", "Epoch 412/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.5633 - lr: 0.0014\n", "Epoch 413/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 8.4406 - lr: 0.0014\n", "Epoch 414/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 10.4421 - lr: 0.0015\n", "Epoch 415/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 8.6437 - lr: 0.0015\n", "Epoch 416/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.1719 - lr: 0.0015\n", "Epoch 417/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 11.8030 - lr: 0.0016\n", "Epoch 418/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 14.0446 - lr: 0.0016\n", "Epoch 419/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 9.6421 - lr: 0.0017\n", "Epoch 420/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 10.9558 - lr: 0.0017\n", "Epoch 421/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 12.0919 - lr: 0.0018\n", "Epoch 422/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 12.2342 - lr: 0.0018\n", "Epoch 423/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 14.6753 - lr: 0.0019\n", "Epoch 424/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 14.0301 - lr: 0.0019\n", "Epoch 425/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 11.6810 - lr: 0.0020\n", "Epoch 426/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 11.3777 - lr: 0.0021\n", "Epoch 427/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 10.4802 - lr: 0.0021\n", "Epoch 428/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 9.0766 - lr: 0.0022\n", "Epoch 429/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 16.9189 - lr: 0.0022\n", "Epoch 430/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 12.6631 - lr: 0.0023\n", "Epoch 431/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 21.4690 - lr: 0.0024\n", "Epoch 432/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 11.0264 - lr: 0.0024\n", "Epoch 433/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 12.3879 - lr: 0.0025\n", "Epoch 434/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 10.4728 - lr: 0.0026\n", "Epoch 435/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 13.9471 - lr: 0.0027\n", "Epoch 436/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 7.6708 - lr: 0.0027\n", "Epoch 437/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.4511 - lr: 0.0028\n", "Epoch 438/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 7.8323 - lr: 0.0029\n", "Epoch 439/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 11.8763 - lr: 0.0030\n", "Epoch 440/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 16.4160 - lr: 0.0031\n", "Epoch 441/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 10.0369 - lr: 0.0032\n", "Epoch 442/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 17.0249 - lr: 0.0033\n", "Epoch 443/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.8089 - lr: 0.0033\n", "Epoch 444/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 16.2504 - lr: 0.0034\n", "Epoch 445/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 11.6259 - lr: 0.0035\n", "Epoch 446/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 11.7379 - lr: 0.0037\n", "Epoch 447/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 10.9446 - lr: 0.0038\n", "Epoch 448/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 14.1374 - lr: 0.0039\n", "Epoch 449/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.7308 - lr: 0.0040\n", "Epoch 450/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 13.1593 - lr: 0.0041\n", "Epoch 451/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.7551 - lr: 0.0042\n", "Epoch 452/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 16.9199 - lr: 0.0043\n", "Epoch 453/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 11.3504 - lr: 0.0045\n", "Epoch 454/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 10.5914 - lr: 0.0046\n", "Epoch 455/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 18.1325 - lr: 0.0047\n", "Epoch 456/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 15.2653 - lr: 0.0049\n", "Epoch 457/600\n", "34/34 [==============================] - 0s 10ms/step - loss: 14.5177 - lr: 0.0050\n", "Epoch 458/600\n", "34/34 [==============================] - 0s 13ms/step - loss: 11.8357 - lr: 0.0052\n", "Epoch 459/600\n", "34/34 [==============================] - 0s 11ms/step - loss: 9.2991 - lr: 0.0053\n", "Epoch 460/600\n", "34/34 [==============================] - 0s 11ms/step - loss: 12.4753 - lr: 0.0055\n", "Epoch 461/600\n", "34/34 [==============================] - 1s 15ms/step - loss: 10.4719 - lr: 0.0056\n", "Epoch 462/600\n", "34/34 [==============================] - 0s 14ms/step - loss: 12.3868 - lr: 0.0058\n", "Epoch 463/600\n", "34/34 [==============================] - 1s 16ms/step - loss: 10.9738 - lr: 0.0060\n", "Epoch 464/600\n", "34/34 [==============================] - 1s 14ms/step - loss: 28.3716 - lr: 0.0061\n", "Epoch 465/600\n", "34/34 [==============================] - 1s 18ms/step - loss: 25.2217 - lr: 0.0063\n", "Epoch 466/600\n", "34/34 [==============================] - 0s 12ms/step - loss: 15.7705 - lr: 0.0065\n", "Epoch 467/600\n", "34/34 [==============================] - 0s 14ms/step - loss: 20.6854 - lr: 0.0067\n", "Epoch 468/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 17.1715 - lr: 0.0069\n", "Epoch 469/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 15.4673 - lr: 0.0071\n", "Epoch 470/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 20.3924 - lr: 0.0073\n", "Epoch 471/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 15.0994 - lr: 0.0075\n", "Epoch 472/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 17.5061 - lr: 0.0077\n", "Epoch 473/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 16.6217 - lr: 0.0079\n", "Epoch 474/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 19.1219 - lr: 0.0082\n", "Epoch 475/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 13.4503 - lr: 0.0084\n", "Epoch 476/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 12.6715 - lr: 0.0087\n", "Epoch 477/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 13.7767 - lr: 0.0089\n", "Epoch 478/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 9.1952 - lr: 0.0092\n", "Epoch 479/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 13.8625 - lr: 0.0094\n", "Epoch 480/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 9.2258 - lr: 0.0097\n", "Epoch 481/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 19.7429 - lr: 0.0100\n", "Epoch 482/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.7022 - lr: 0.0103\n", "Epoch 483/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 27.9510 - lr: 0.0106\n", "Epoch 484/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 15.0957 - lr: 0.0109\n", "Epoch 485/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 16.6445 - lr: 0.0112\n", "Epoch 486/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 17.8369 - lr: 0.0115\n", "Epoch 487/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 17.1466 - lr: 0.0119\n", "Epoch 488/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 22.4863 - lr: 0.0122\n", "Epoch 489/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 20.0853 - lr: 0.0126\n", "Epoch 490/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.8547 - lr: 0.0130\n", "Epoch 491/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 16.0194 - lr: 0.0133\n", "Epoch 492/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 17.6666 - lr: 0.0137\n", "Epoch 493/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 22.3339 - lr: 0.0141\n", "Epoch 494/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 15.0573 - lr: 0.0145\n", "Epoch 495/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 16.1600 - lr: 0.0150\n", "Epoch 496/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 25.9704 - lr: 0.0154\n", "Epoch 497/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 26.0468 - lr: 0.0158\n", "Epoch 498/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 28.1070 - lr: 0.0163\n", "Epoch 499/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 16.3354 - lr: 0.0168\n", "Epoch 500/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.7099 - lr: 0.0173\n", "Epoch 501/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 19.1367 - lr: 0.0178\n", "Epoch 502/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 24.1536 - lr: 0.0183\n", "Epoch 503/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 14.3085 - lr: 0.0188\n", "Epoch 504/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 15.7657 - lr: 0.0194\n", "Epoch 505/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 23.4117 - lr: 0.0200\n", "Epoch 506/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 23.8955 - lr: 0.0205\n", "Epoch 507/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 21.8793 - lr: 0.0211\n", "Epoch 508/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 30.0722 - lr: 0.0218\n", "Epoch 509/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 18.1899 - lr: 0.0224\n", "Epoch 510/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 14.8232 - lr: 0.0230\n", "Epoch 511/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 28.7277 - lr: 0.0237\n", "Epoch 512/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 31.1979 - lr: 0.0244\n", "Epoch 513/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 25.8386 - lr: 0.0251\n", "Epoch 514/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 16.0727 - lr: 0.0259\n", "Epoch 515/600\n", "34/34 [==============================] - 0s 12ms/step - loss: 32.2033 - lr: 0.0266\n", "Epoch 516/600\n", "34/34 [==============================] - 0s 13ms/step - loss: 26.5906 - lr: 0.0274\n", "Epoch 517/600\n", "34/34 [==============================] - 0s 10ms/step - loss: 24.5355 - lr: 0.0282\n", "Epoch 518/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 22.9203 - lr: 0.0290\n", "Epoch 519/600\n", "34/34 [==============================] - 0s 13ms/step - loss: 17.2017 - lr: 0.0299\n", "Epoch 520/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 23.5763 - lr: 0.0307\n", "Epoch 521/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 26.6647 - lr: 0.0316\n", "Epoch 522/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 25.4308 - lr: 0.0325\n", "Epoch 523/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 23.9982 - lr: 0.0335\n", "Epoch 524/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 23.0654 - lr: 0.0345\n", "Epoch 525/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 22.5344 - lr: 0.0355\n", "Epoch 526/600\n", "34/34 [==============================] - 0s 10ms/step - loss: 25.9911 - lr: 0.0365\n", "Epoch 527/600\n", "34/34 [==============================] - 0s 12ms/step - loss: 23.7654 - lr: 0.0376\n", "Epoch 528/600\n", "34/34 [==============================] - 0s 11ms/step - loss: 39.3303 - lr: 0.0387\n", "Epoch 529/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 37.3932 - lr: 0.0398\n", "Epoch 530/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.8136 - lr: 0.0410\n", "Epoch 531/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 38.4851 - lr: 0.0422\n", "Epoch 532/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 38.4319 - lr: 0.0434\n", "Epoch 533/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 37.4968 - lr: 0.0447\n", "Epoch 534/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 37.0067 - lr: 0.0460\n", "Epoch 535/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 38.0261 - lr: 0.0473\n", "Epoch 536/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 38.8769 - lr: 0.0487\n", "Epoch 537/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 38.9150 - lr: 0.0501\n", "Epoch 538/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 37.0741 - lr: 0.0516\n", "Epoch 539/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 39.9940 - lr: 0.0531\n", "Epoch 540/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 49.4227 - lr: 0.0546\n", "Epoch 541/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 48.8562 - lr: 0.0562\n", "Epoch 542/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 40.1680 - lr: 0.0579\n", "Epoch 543/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 44.3165 - lr: 0.0596\n", "Epoch 544/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 37.6835 - lr: 0.0613\n", "Epoch 545/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 44.8412 - lr: 0.0631\n", "Epoch 546/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 46.2593 - lr: 0.0649\n", "Epoch 547/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 41.1220 - lr: 0.0668\n", "Epoch 548/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 43.2803 - lr: 0.0688\n", "Epoch 549/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 39.8500 - lr: 0.0708\n", "Epoch 550/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 38.9956 - lr: 0.0729\n", "Epoch 551/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 40.1656 - lr: 0.0750\n", "Epoch 552/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 41.0395 - lr: 0.0772\n", "Epoch 553/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 37.6290 - lr: 0.0794\n", "Epoch 554/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 40.2458 - lr: 0.0818\n", "Epoch 555/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 41.8614 - lr: 0.0841\n", "Epoch 556/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 42.2682 - lr: 0.0866\n", "Epoch 557/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 46.2143 - lr: 0.0891\n", "Epoch 558/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 42.0625 - lr: 0.0917\n", "Epoch 559/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 44.0059 - lr: 0.0944\n", "Epoch 560/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 44.4258 - lr: 0.0972\n", "Epoch 561/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 43.0777 - lr: 0.1000\n", "Epoch 562/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 46.7122 - lr: 0.1029\n", "Epoch 563/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 39.6288 - lr: 0.1059\n", "Epoch 564/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 38.5653 - lr: 0.1090\n", "Epoch 565/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 66.8065 - lr: 0.1122\n", "Epoch 566/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 44.4002 - lr: 0.1155\n", "Epoch 567/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 43.3258 - lr: 0.1189\n", "Epoch 568/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 53.7262 - lr: 0.1223\n", "Epoch 569/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 42.5474 - lr: 0.1259\n", "Epoch 570/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 72.5207 - lr: 0.1296\n", "Epoch 571/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 48.1272 - lr: 0.1334\n", "Epoch 572/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 50.8717 - lr: 0.1372\n", "Epoch 573/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 46.9161 - lr: 0.1413\n", "Epoch 574/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 52.2768 - lr: 0.1454\n", "Epoch 575/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 53.6321 - lr: 0.1496\n", "Epoch 576/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 46.9893 - lr: 0.1540\n", "Epoch 577/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 45.9002 - lr: 0.1585\n", "Epoch 578/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 51.7023 - lr: 0.1631\n", "Epoch 579/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 50.9754 - lr: 0.1679\n", "Epoch 580/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 44.7651 - lr: 0.1728\n", "Epoch 581/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 53.3125 - lr: 0.1778\n", "Epoch 582/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 64.0657 - lr: 0.1830\n", "Epoch 583/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 54.2182 - lr: 0.1884\n", "Epoch 584/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 59.1986 - lr: 0.1939\n", "Epoch 585/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 64.8373 - lr: 0.1995\n", "Epoch 586/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 59.2909 - lr: 0.2054\n", "Epoch 587/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 60.2097 - lr: 0.2113\n", "Epoch 588/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 55.6389 - lr: 0.2175\n", "Epoch 589/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 73.9964 - lr: 0.2239\n", "Epoch 590/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 49.7493 - lr: 0.2304\n", "Epoch 591/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 55.3316 - lr: 0.2371\n", "Epoch 592/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 60.2859 - lr: 0.2441\n", "Epoch 593/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 88.0011 - lr: 0.2512\n", "Epoch 594/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 49.0861 - lr: 0.2585\n", "Epoch 595/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 72.9109 - lr: 0.2661\n", "Epoch 596/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 60.1341 - lr: 0.2738\n", "Epoch 597/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 44.3118 - lr: 0.2818\n", "Epoch 598/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 61.8647 - lr: 0.2901\n", "Epoch 599/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 102.8436 - lr: 0.2985\n", "Epoch 600/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 49.9601 - lr: 0.3073\n" ] } ], "source": [ "# 設置學習率調節器\n", "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n", " lambda epoch: 1e-8 * 10**(epoch / 80))\n", "\n", "# 設置優化器，這邊選擇 Adam\n", "optimizer = tf.keras.optimizers.Adam()\n", "\n", "# 編譯模型並選擇 Huber loss\n", "model_tune.compile(loss=tf.keras.losses.Huber(), optimizer=optimizer)\n", "\n", "# 訓練模型\n", "history = model_tune.fit(\n", " train_loader, \n", " epochs=600, \n", " callbacks=[lr_schedule])" ] }, { "cell_type": "markdown", "id": "0f63db72", "metadata": { "id": "0f63db72" }, "source": [ "### Plot Loss vs Learning Rate\n", "\n", "下面可以看到 loss 在每個階段隨著不同 learning rate 的變化
\n", "之後我們可以選擇一個使 loss 下降較快的 learning rate 作為模型訓練的初始 learning rate" ] }, { "cell_type": "code", "execution_count": 8, "id": "07784842", "metadata": { "id": "07784842", "outputId": "29b5c61f-7541-42c7-cab3-3476bfb76b5c", "colab": { "base_uri": "https://localhost:8080/", "height": 542 } }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "# 定義 learning rate array\n", "lrs = 1e-8 * (10 ** (np.arange(600) / 80))\n", "\n", "\n", "fig = px.line(x=lrs, y=history.history[\"loss\"], log_x=True)\n", "# fig = px.line(x=lrs, y=history.history[\"loss\"])\n", "fig.update_layout(title='Loss vs Learning Rate', xaxis_title='Learning Rate', yaxis_title='Loss')\n", "fig.update_xaxes(tickformat='0.1e')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "d6263685", "metadata": { "id": "d6263685" }, "source": [ "## Model Training" ] }, { "cell_type": "code", "execution_count": 9, "id": "4087b955", "metadata": { "id": "4087b955", "outputId": "609174d6-0d1e-453f-ab02-c89af3ac6aba", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/600\n", "34/34 [==============================] - 2s 6ms/step - loss: 645.0530 - mae: 645.5530 - lr: 1.0000e-05\n", "Epoch 2/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 629.2055 - mae: 629.7055 - lr: 1.0000e-05\n", "Epoch 3/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 612.3815 - mae: 612.8815 - lr: 1.0000e-05\n", "Epoch 4/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 594.8000 - mae: 595.3000 - lr: 1.0000e-05\n", "Epoch 5/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 576.9542 - mae: 577.4542 - lr: 1.0000e-05\n", "Epoch 6/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 559.4575 - mae: 559.9575 - lr: 1.0000e-05\n", "Epoch 7/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 542.7781 - mae: 543.2781 - lr: 1.0000e-05\n", "Epoch 8/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 527.1353 - mae: 527.6353 - lr: 1.0000e-05\n", "Epoch 9/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 512.5450 - mae: 513.0450 - lr: 1.0000e-05\n", "Epoch 10/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 498.9084 - mae: 499.4084 - lr: 1.0000e-05\n", "Epoch 11/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 486.0895 - mae: 486.5895 - lr: 1.0000e-05\n", "Epoch 12/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 473.9718 - mae: 474.4718 - lr: 1.0000e-05\n", "Epoch 13/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 462.3878 - mae: 462.8878 - lr: 1.0000e-05\n", "Epoch 14/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 451.1665 - mae: 451.6665 - lr: 1.0000e-05\n", "Epoch 15/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 440.2361 - mae: 440.7361 - lr: 1.0000e-05\n", "Epoch 16/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 429.5226 - mae: 430.0226 - lr: 1.0000e-05\n", "Epoch 17/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 418.9547 - mae: 419.4547 - lr: 1.0000e-05\n", "Epoch 18/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 408.4704 - mae: 408.9704 - lr: 1.0000e-05\n", "Epoch 19/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 398.0169 - mae: 398.5169 - lr: 1.0000e-05\n", "Epoch 20/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 387.5489 - mae: 388.0489 - lr: 1.0000e-05\n", "Epoch 21/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 377.0289 - mae: 377.5289 - lr: 1.0000e-05\n", "Epoch 22/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 366.4278 - mae: 366.9278 - lr: 1.0000e-05\n", "Epoch 23/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 355.7288 - mae: 356.2288 - lr: 1.0000e-05\n", "Epoch 24/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 344.9340 - mae: 345.4340 - lr: 1.0000e-05\n", "Epoch 25/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 334.0747 - mae: 334.5747 - lr: 1.0000e-05\n", "Epoch 26/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 323.2227 - mae: 323.7227 - lr: 1.0000e-05\n", "Epoch 27/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 312.4935 - mae: 312.9935 - lr: 1.0000e-05\n", "Epoch 28/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 302.0285 - mae: 302.5285 - lr: 1.0000e-05\n", "Epoch 29/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 291.9578 - mae: 292.4578 - lr: 1.0000e-05\n", "Epoch 30/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 282.3618 - mae: 282.8618 - lr: 1.0000e-05\n", "Epoch 31/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 273.2560 - mae: 273.7560 - lr: 1.0000e-05\n", "Epoch 32/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 264.6023 - mae: 265.1023 - lr: 1.0000e-05\n", "Epoch 33/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 256.3349 - mae: 256.8349 - lr: 1.0000e-05\n", "Epoch 34/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 248.3815 - mae: 248.8815 - lr: 1.0000e-05\n", "Epoch 35/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 240.6752 - mae: 241.1752 - lr: 1.0000e-05\n", "Epoch 36/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 233.1589 - mae: 233.6589 - lr: 1.0000e-05\n", "Epoch 37/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 225.7856 - mae: 226.2856 - lr: 1.0000e-05\n", "Epoch 38/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 218.5165 - mae: 219.0165 - lr: 1.0000e-05\n", "Epoch 39/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 211.3195 - mae: 211.8195 - lr: 1.0000e-05\n", "Epoch 40/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 204.1679 - mae: 204.6679 - lr: 1.0000e-05\n", "Epoch 41/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 197.0385 - mae: 197.5385 - lr: 1.0000e-05\n", "Epoch 42/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 189.9114 - mae: 190.4114 - lr: 1.0000e-05\n", "Epoch 43/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 182.7686 - mae: 183.2686 - lr: 1.0000e-05\n", "Epoch 44/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 175.5935 - mae: 176.0935 - lr: 1.0000e-05\n", "Epoch 45/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 168.3713 - mae: 168.8713 - lr: 1.0000e-05\n", "Epoch 46/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 161.0887 - mae: 161.5887 - lr: 1.0000e-05\n", "Epoch 47/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 153.7347 - mae: 154.2347 - lr: 1.0000e-05\n", "Epoch 48/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 146.3014 - mae: 146.8014 - lr: 1.0000e-05\n", "Epoch 49/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 138.7863 - mae: 139.2863 - lr: 1.0000e-05\n", "Epoch 50/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 131.1937 - mae: 131.6937 - lr: 1.0000e-05\n", "Epoch 51/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 123.5384 - mae: 124.0384 - lr: 1.0000e-05\n", "Epoch 52/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 115.8458 - mae: 116.3458 - lr: 1.0000e-05\n", "Epoch 53/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 108.1516 - mae: 108.6516 - lr: 1.0000e-05\n", "Epoch 54/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 100.4959 - mae: 100.9959 - lr: 1.0000e-05\n", "Epoch 55/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 92.9693 - mae: 93.4693 - lr: 1.0000e-05\n", "Epoch 56/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 85.7718 - mae: 86.2704 - lr: 1.0000e-05\n", "Epoch 57/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 79.1513 - mae: 79.6506 - lr: 1.0000e-05\n", "Epoch 58/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 73.0929 - mae: 73.5919 - lr: 1.0000e-05\n", "Epoch 59/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 67.5894 - mae: 68.0883 - lr: 1.0000e-05\n", "Epoch 60/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 62.7393 - mae: 63.2379 - lr: 1.0000e-05\n", "Epoch 61/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 58.6728 - mae: 59.1718 - lr: 1.0000e-05\n", "Epoch 62/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 55.1584 - mae: 55.6577 - lr: 1.0000e-05\n", "Epoch 63/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 52.0620 - mae: 52.5614 - lr: 1.0000e-05\n", "Epoch 64/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 49.3649 - mae: 49.8620 - lr: 1.0000e-05\n", "Epoch 65/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 47.0605 - mae: 47.5566 - lr: 1.0000e-05\n", "Epoch 66/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 45.1553 - mae: 45.6533 - lr: 1.0000e-05\n", "Epoch 67/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 43.5033 - mae: 44.0004 - lr: 1.0000e-05\n", "Epoch 68/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 42.1079 - mae: 42.6053 - lr: 1.0000e-05\n", "Epoch 69/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 40.9302 - mae: 41.4286 - lr: 1.0000e-05\n", "Epoch 70/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 39.9688 - mae: 40.4664 - lr: 1.0000e-05\n", "Epoch 71/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 39.1862 - mae: 39.6831 - lr: 1.0000e-05\n", "Epoch 72/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 38.5331 - mae: 39.0309 - lr: 1.0000e-05\n", "Epoch 73/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 37.9937 - mae: 38.4912 - lr: 1.0000e-05\n", "Epoch 74/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 37.5721 - mae: 38.0693 - lr: 1.0000e-05\n", "Epoch 75/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 37.2132 - mae: 37.7097 - lr: 1.0000e-05\n", "Epoch 76/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 36.9461 - mae: 37.4431 - lr: 1.0000e-05\n", "Epoch 77/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.7628 - mae: 37.2599 - lr: 1.0000e-05\n", "Epoch 78/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.6039 - mae: 37.1017 - lr: 1.0000e-05\n", "Epoch 79/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.4856 - mae: 36.9820 - lr: 1.0000e-05\n", "Epoch 80/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.3952 - mae: 36.8911 - lr: 1.0000e-05\n", "Epoch 81/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.3333 - mae: 36.8307 - lr: 1.0000e-05\n", "Epoch 82/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.2882 - mae: 36.7863 - lr: 1.0000e-05\n", "Epoch 83/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.2496 - mae: 36.7463 - lr: 1.0000e-05\n", "Epoch 84/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.2219 - mae: 36.7190 - lr: 1.0000e-05\n", "Epoch 85/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.2006 - mae: 36.6983 - lr: 1.0000e-05\n", "Epoch 86/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1854 - mae: 36.6828 - lr: 1.0000e-05\n", "Epoch 87/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1726 - mae: 36.6694 - lr: 1.0000e-05\n", "Epoch 88/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1638 - mae: 36.6598 - lr: 1.0000e-05\n", "Epoch 89/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1570 - mae: 36.6531 - lr: 1.0000e-05\n", "Epoch 90/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1543 - mae: 36.6504 - lr: 1.0000e-05\n", "Epoch 91/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1506 - mae: 36.6463 - lr: 1.0000e-05\n", "Epoch 92/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1499 - mae: 36.6458 - lr: 1.0000e-05\n", "Epoch 93/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1506 - mae: 36.6464 - lr: 1.0000e-05\n", "Epoch 94/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1458 - mae: 36.6415 - lr: 1.0000e-05\n", "Epoch 95/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1448 - mae: 36.6409 - lr: 1.0000e-05\n", "Epoch 96/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1463 - mae: 36.6426 - lr: 1.0000e-05\n", "Epoch 97/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 36.1423 - mae: 36.6384 - lr: 1.0000e-05\n", "Epoch 98/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1422 - mae: 36.6382 - lr: 1.0000e-05\n", "Epoch 99/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1382 - mae: 36.6341 - lr: 1.0000e-05\n", "Epoch 100/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.1318 - mae: 36.6278 - lr: 1.0000e-05\n", "Epoch 101/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 36.0971 - mae: 36.5932 - lr: 1.0000e-05\n", "Epoch 102/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 35.8559 - mae: 36.3526 - lr: 1.0000e-05\n", "Epoch 103/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 35.3037 - mae: 35.8018 - lr: 1.0000e-05\n", "Epoch 104/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 35.0065 - mae: 35.5034 - lr: 1.0000e-05\n", "Epoch 105/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 34.8464 - mae: 35.3427 - lr: 1.0000e-05\n", "Epoch 106/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 34.7359 - mae: 35.2326 - lr: 1.0000e-05\n", "Epoch 107/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 34.6021 - mae: 35.0984 - lr: 1.0000e-05\n", "Epoch 108/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 34.4919 - mae: 34.9903 - lr: 1.0000e-05\n", "Epoch 109/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 34.4040 - mae: 34.9021 - lr: 1.0000e-05\n", "Epoch 110/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 34.3041 - mae: 34.8020 - lr: 1.0000e-05\n", "Epoch 111/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 34.2253 - mae: 34.7226 - lr: 1.0000e-05\n", "Epoch 112/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 34.1406 - mae: 34.6373 - lr: 1.0000e-05\n", "Epoch 113/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 34.0647 - mae: 34.5611 - lr: 1.0000e-05\n", "Epoch 114/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 34.0037 - mae: 34.5003 - lr: 1.0000e-05\n", "Epoch 115/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 33.9345 - mae: 34.4315 - lr: 1.0000e-05\n", "Epoch 116/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 33.8774 - mae: 34.3744 - lr: 1.0000e-05\n", "Epoch 117/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 33.8190 - mae: 34.3153 - lr: 1.0000e-05\n", "Epoch 118/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 33.7502 - mae: 34.2473 - lr: 1.0000e-05\n", "Epoch 119/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 33.6809 - mae: 34.1775 - lr: 1.0000e-05\n", "Epoch 120/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 33.6339 - mae: 34.1312 - lr: 1.0000e-05\n", "Epoch 121/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 33.5664 - mae: 34.0631 - lr: 1.0000e-05\n", "Epoch 122/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 33.4762 - mae: 33.9732 - lr: 1.0000e-05\n", "Epoch 123/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 33.3741 - mae: 33.8702 - lr: 1.0000e-05\n", "Epoch 124/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 33.2741 - mae: 33.7710 - lr: 1.0000e-05\n", "Epoch 125/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 33.1206 - mae: 33.6170 - lr: 1.0000e-05\n", "Epoch 126/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 32.9808 - mae: 33.4771 - lr: 1.0000e-05\n", "Epoch 127/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 32.7368 - mae: 33.2346 - lr: 1.0000e-05\n", "Epoch 128/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 32.5633 - mae: 33.0596 - lr: 1.0000e-05\n", "Epoch 129/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 32.3184 - mae: 32.8150 - lr: 1.0000e-05\n", "Epoch 130/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 32.1690 - mae: 32.6659 - lr: 1.0000e-05\n", "Epoch 131/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 32.1279 - mae: 32.6252 - lr: 1.0000e-05\n", "Epoch 132/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 32.0311 - mae: 32.5275 - lr: 1.0000e-05\n", "Epoch 133/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.9468 - mae: 32.4433 - lr: 1.0000e-05\n", "Epoch 134/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.8124 - mae: 32.3097 - lr: 1.0000e-05\n", "Epoch 135/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.7480 - mae: 32.2439 - lr: 1.0000e-05\n", "Epoch 136/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.6732 - mae: 32.1710 - lr: 1.0000e-05\n", "Epoch 137/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 31.5817 - mae: 32.0789 - lr: 1.0000e-05\n", "Epoch 138/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.4974 - mae: 31.9937 - lr: 1.0000e-05\n", "Epoch 139/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 31.3908 - mae: 31.8887 - lr: 1.0000e-05\n", "Epoch 140/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.3771 - mae: 31.8741 - lr: 1.0000e-05\n", "Epoch 141/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.2704 - mae: 31.7677 - lr: 1.0000e-05\n", "Epoch 142/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.1412 - mae: 31.6370 - lr: 1.0000e-05\n", "Epoch 143/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 31.0623 - mae: 31.5589 - lr: 1.0000e-05\n", "Epoch 144/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.9867 - mae: 31.4832 - lr: 1.0000e-05\n", "Epoch 145/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.9004 - mae: 31.3961 - lr: 1.0000e-05\n", "Epoch 146/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.8003 - mae: 31.2972 - lr: 1.0000e-05\n", "Epoch 147/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.7175 - mae: 31.2133 - lr: 1.0000e-05\n", "Epoch 148/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 30.6235 - mae: 31.1205 - lr: 1.0000e-05\n", "Epoch 149/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.5397 - mae: 31.0363 - lr: 1.0000e-05\n", "Epoch 150/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.4242 - mae: 30.9210 - lr: 1.0000e-05\n", "Epoch 151/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.3117 - mae: 30.8093 - lr: 1.0000e-05\n", "Epoch 152/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.2972 - mae: 30.7934 - lr: 1.0000e-05\n", "Epoch 153/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 30.0892 - mae: 30.5854 - lr: 1.0000e-05\n", "Epoch 154/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 29.9975 - mae: 30.4941 - lr: 1.0000e-05\n", "Epoch 155/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 29.8621 - mae: 30.3583 - lr: 1.0000e-05\n", "Epoch 156/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 29.7240 - mae: 30.2198 - lr: 1.0000e-05\n", "Epoch 157/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 29.5937 - mae: 30.0908 - lr: 1.0000e-05\n", "Epoch 158/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 29.4762 - mae: 29.9709 - lr: 1.0000e-05\n", "Epoch 159/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 29.3418 - mae: 29.8364 - lr: 1.0000e-05\n", "Epoch 160/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 29.1859 - mae: 29.6827 - lr: 1.0000e-05\n", "Epoch 161/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 29.0516 - mae: 29.5481 - lr: 1.0000e-05\n", "Epoch 162/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 28.8836 - mae: 29.3793 - lr: 1.0000e-05\n", "Epoch 163/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 28.7433 - mae: 29.2399 - lr: 1.0000e-05\n", "Epoch 164/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 28.6061 - mae: 29.1017 - lr: 1.0000e-05\n", "Epoch 165/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 28.3901 - mae: 28.8855 - lr: 1.0000e-05\n", "Epoch 166/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 28.2015 - mae: 28.6985 - lr: 1.0000e-05\n", "Epoch 167/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 28.0778 - mae: 28.5751 - lr: 1.0000e-05\n", "Epoch 168/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 27.8535 - mae: 28.3470 - lr: 1.0000e-05\n", "Epoch 169/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 27.6965 - mae: 28.1935 - lr: 1.0000e-05\n", "Epoch 170/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 27.3623 - mae: 27.8579 - lr: 1.0000e-05\n", "Epoch 171/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 27.2374 - mae: 27.7308 - lr: 1.0000e-05\n", "Epoch 172/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 27.0480 - mae: 27.5438 - lr: 1.0000e-05\n", "Epoch 173/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 26.7188 - mae: 27.2159 - lr: 1.0000e-05\n", "Epoch 174/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 26.5083 - mae: 27.0052 - lr: 1.0000e-05\n", "Epoch 175/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 26.2652 - mae: 26.7610 - lr: 1.0000e-05\n", "Epoch 176/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 26.0445 - mae: 26.5403 - lr: 1.0000e-05\n", "Epoch 177/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 25.7962 - mae: 26.2927 - lr: 1.0000e-05\n", "Epoch 178/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 25.5027 - mae: 25.9979 - lr: 1.0000e-05\n", "Epoch 179/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 25.3006 - mae: 25.7984 - lr: 1.0000e-05\n", "Epoch 180/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 24.9581 - mae: 25.4546 - lr: 1.0000e-05\n", "Epoch 181/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 24.6507 - mae: 25.1456 - lr: 1.0000e-05\n", "Epoch 182/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 24.3202 - mae: 24.8163 - lr: 1.0000e-05\n", "Epoch 183/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 24.0925 - mae: 24.5881 - lr: 1.0000e-05\n", "Epoch 184/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 23.8013 - mae: 24.2974 - lr: 1.0000e-05\n", "Epoch 185/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 23.4559 - mae: 23.9496 - lr: 1.0000e-05\n", "Epoch 186/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 23.1544 - mae: 23.6494 - lr: 1.0000e-05\n", "Epoch 187/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 22.9162 - mae: 23.4114 - lr: 1.0000e-05\n", "Epoch 188/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 22.5164 - mae: 23.0109 - lr: 1.0000e-05\n", "Epoch 189/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 22.1983 - mae: 22.6935 - lr: 1.0000e-05\n", "Epoch 190/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 21.8709 - mae: 22.3661 - lr: 1.0000e-05\n", "Epoch 191/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 21.5827 - mae: 22.0785 - lr: 1.0000e-05\n", "Epoch 192/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 21.2070 - mae: 21.7032 - lr: 1.0000e-05\n", "Epoch 193/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 20.8862 - mae: 21.3791 - lr: 1.0000e-05\n", "Epoch 194/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 20.5060 - mae: 21.0009 - lr: 1.0000e-05\n", "Epoch 195/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 20.1655 - mae: 20.6593 - lr: 1.0000e-05\n", "Epoch 196/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 19.9412 - mae: 20.4344 - lr: 1.0000e-05\n", "Epoch 197/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 19.4423 - mae: 19.9348 - lr: 1.0000e-05\n", "Epoch 198/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 19.1916 - mae: 19.6870 - lr: 1.0000e-05\n", "Epoch 199/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 18.7267 - mae: 19.2213 - lr: 1.0000e-05\n", "Epoch 200/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 18.3936 - mae: 18.8856 - lr: 1.0000e-05\n", "Epoch 201/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 17.9907 - mae: 18.4833 - lr: 1.0000e-05\n", "Epoch 202/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 17.7657 - mae: 18.2594 - lr: 1.0000e-05\n", "Epoch 203/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 17.4348 - mae: 17.9285 - lr: 1.0000e-05\n", "Epoch 204/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 16.8615 - mae: 17.3547 - lr: 1.0000e-05\n", "Epoch 205/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 16.3477 - mae: 16.8397 - lr: 1.0000e-05\n", "Epoch 206/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 15.8876 - mae: 16.3804 - lr: 1.0000e-05\n", "Epoch 207/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 15.4212 - mae: 15.9152 - lr: 1.0000e-05\n", "Epoch 208/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 14.9582 - mae: 15.4507 - lr: 1.0000e-05\n", "Epoch 209/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 14.5471 - mae: 15.0402 - lr: 1.0000e-05\n", "Epoch 210/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 13.9929 - mae: 14.4847 - lr: 1.0000e-05\n", "Epoch 211/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 13.5595 - mae: 14.0540 - lr: 1.0000e-05\n", "Epoch 212/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 13.0162 - mae: 13.5063 - lr: 1.0000e-05\n", "Epoch 213/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 12.5414 - mae: 13.0311 - lr: 1.0000e-05\n", "Epoch 214/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 12.0201 - mae: 12.5102 - lr: 1.0000e-05\n", "Epoch 215/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 11.4672 - mae: 11.9600 - lr: 1.0000e-05\n", "Epoch 216/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 11.0170 - mae: 11.5058 - lr: 1.0000e-05\n", "Epoch 217/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 10.5724 - mae: 11.0629 - lr: 1.0000e-05\n", "Epoch 218/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.9873 - mae: 10.4792 - lr: 1.0000e-05\n", "Epoch 219/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 9.5884 - mae: 10.0746 - lr: 1.0000e-05\n", "Epoch 220/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 9.0537 - mae: 9.5415 - lr: 1.0000e-05\n", "Epoch 221/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 8.5150 - mae: 8.9984 - lr: 1.0000e-05\n", "Epoch 222/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 8.1432 - mae: 8.6321 - lr: 1.0000e-05\n", "Epoch 223/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 8.1005 - mae: 8.5907 - lr: 1.0000e-05\n", "Epoch 224/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 7.5277 - mae: 8.0158 - lr: 1.0000e-05\n", "Epoch 225/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 7.0482 - mae: 7.5344 - lr: 1.0000e-05\n", "Epoch 226/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.7917 - mae: 7.2750 - lr: 1.0000e-05\n", "Epoch 227/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.5367 - mae: 7.0195 - lr: 1.0000e-05\n", "Epoch 228/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.3546 - mae: 6.8400 - lr: 1.0000e-05\n", "Epoch 229/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.2199 - mae: 6.7036 - lr: 1.0000e-05\n", "Epoch 230/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 6.1585 - mae: 6.6395 - lr: 1.0000e-05\n", "Epoch 231/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.9107 - mae: 6.3933 - lr: 1.0000e-05\n", "Epoch 232/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.8459 - mae: 6.3285 - lr: 1.0000e-05\n", "Epoch 233/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.7746 - mae: 6.2577 - lr: 1.0000e-05\n", "Epoch 234/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.5923 - mae: 6.0734 - lr: 1.0000e-05\n", "Epoch 235/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.6117 - mae: 6.0919 - lr: 1.0000e-05\n", "Epoch 236/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.7088 - mae: 6.1914 - lr: 1.0000e-05\n", "Epoch 237/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.5227 - mae: 6.0013 - lr: 1.0000e-05\n", "Epoch 238/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.4770 - mae: 5.9593 - lr: 1.0000e-05\n", "Epoch 239/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.3364 - mae: 5.8164 - lr: 1.0000e-05\n", "Epoch 240/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.3319 - mae: 5.8104 - lr: 1.0000e-05\n", "Epoch 241/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 5.2810 - mae: 5.7598 - lr: 1.0000e-05\n", "Epoch 242/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 5.2640 - mae: 5.7464 - lr: 1.0000e-05\n", "Epoch 243/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 5.2040 - mae: 5.6806 - lr: 1.0000e-05\n", "Epoch 244/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 5.3044 - mae: 5.7840 - lr: 1.0000e-05\n", "Epoch 245/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 5.2843 - mae: 5.7663 - lr: 1.0000e-05\n", "Epoch 246/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 5.2569 - mae: 5.7372 - lr: 1.0000e-05\n", "Epoch 247/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 5.1446 - mae: 5.6241 - lr: 1.0000e-05\n", "Epoch 248/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.1908 - mae: 5.6715 - lr: 1.0000e-05\n", "Epoch 249/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.1147 - mae: 5.5967 - lr: 1.0000e-05\n", "Epoch 250/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.1003 - mae: 5.5779 - lr: 1.0000e-05\n", "Epoch 251/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.2046 - mae: 5.6895 - lr: 1.0000e-05\n", "Epoch 252/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.0180 - mae: 5.4960 - lr: 1.0000e-05\n", "Epoch 253/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.0479 - mae: 5.5284 - lr: 1.0000e-05\n", "Epoch 254/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.0098 - mae: 5.4879 - lr: 1.0000e-05\n", "Epoch 255/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.0151 - mae: 5.4962 - lr: 1.0000e-05\n", "Epoch 256/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.1998 - mae: 5.6804 - lr: 1.0000e-05\n", "Epoch 257/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9797 - mae: 5.4576 - lr: 1.0000e-05\n", "Epoch 258/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9788 - mae: 5.4577 - lr: 1.0000e-05\n", "Epoch 259/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.9597 - mae: 5.4383 - lr: 1.0000e-05\n", "Epoch 260/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.0279 - mae: 5.5038 - lr: 1.0000e-05\n", "Epoch 261/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9862 - mae: 5.4696 - lr: 1.0000e-05\n", "Epoch 262/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9479 - mae: 5.4261 - lr: 1.0000e-05\n", "Epoch 263/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.9836 - mae: 5.4659 - lr: 1.0000e-05\n", "Epoch 264/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 5.0060 - mae: 5.4799 - lr: 1.0000e-05\n", "Epoch 265/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 5.0006 - mae: 5.4836 - lr: 1.0000e-05\n", "Epoch 266/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.9014 - mae: 5.3825 - lr: 1.0000e-05\n", "Epoch 267/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9617 - mae: 5.4422 - lr: 1.0000e-05\n", "Epoch 268/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9130 - mae: 5.3921 - lr: 1.0000e-05\n", "Epoch 269/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.8855 - mae: 5.3684 - lr: 1.0000e-05\n", "Epoch 270/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9147 - mae: 5.3955 - lr: 1.0000e-05\n", "Epoch 271/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.9670 - mae: 5.4483 - lr: 1.0000e-05\n", "Epoch 272/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.8482 - mae: 5.3276 - lr: 1.0000e-05\n", "Epoch 273/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.8512 - mae: 5.3338 - lr: 1.0000e-05\n", "Epoch 274/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.8102 - mae: 5.2941 - lr: 1.0000e-05\n", "Epoch 275/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.8839 - mae: 5.3625 - lr: 1.0000e-05\n", "Epoch 276/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.8137 - mae: 5.2987 - lr: 1.0000e-05\n", "Epoch 277/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.8322 - mae: 5.3144 - lr: 1.0000e-05\n", "Epoch 278/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.8078 - mae: 5.2923 - lr: 1.0000e-05\n", "Epoch 279/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7945 - mae: 5.2788 - lr: 1.0000e-05\n", "Epoch 280/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.8617 - mae: 5.3395 - lr: 1.0000e-05\n", "Epoch 281/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.8344 - mae: 5.3190 - lr: 1.0000e-05\n", "Epoch 282/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.8094 - mae: 5.2894 - lr: 1.0000e-05\n", "Epoch 283/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 4.8468 - mae: 5.3276 - lr: 1.0000e-05\n", "Epoch 284/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.8608 - mae: 5.3394 - lr: 1.0000e-05\n", "Epoch 285/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7608 - mae: 5.2431 - lr: 1.0000e-06\n", "Epoch 286/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7423 - mae: 5.2267 - lr: 1.0000e-06\n", "Epoch 287/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 4.7415 - mae: 5.2254 - lr: 1.0000e-06\n", "Epoch 288/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7423 - mae: 5.2219 - lr: 1.0000e-06\n", "Epoch 289/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 4.7524 - mae: 5.2369 - lr: 1.0000e-06\n", "Epoch 290/600\n", "34/34 [==============================] - 0s 10ms/step - loss: 4.7422 - mae: 5.2270 - lr: 1.0000e-06\n", "Epoch 291/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7454 - mae: 5.2304 - lr: 1.0000e-06\n", "Epoch 292/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7436 - mae: 5.2287 - lr: 1.0000e-06\n", "Epoch 293/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7337 - mae: 5.2188 - lr: 1.0000e-07\n", "Epoch 294/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7346 - mae: 5.2200 - lr: 1.0000e-07\n", "Epoch 295/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7330 - mae: 5.2184 - lr: 1.0000e-07\n", "Epoch 296/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7338 - mae: 5.2191 - lr: 1.0000e-07\n", "Epoch 297/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7334 - mae: 5.2188 - lr: 1.0000e-07\n", "Epoch 298/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7334 - mae: 5.2186 - lr: 1.0000e-07\n", "Epoch 299/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7338 - mae: 5.2191 - lr: 1.0000e-07\n", "Epoch 300/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7337 - mae: 5.2192 - lr: 1.0000e-07\n", "Epoch 301/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7328 - mae: 5.2182 - lr: 1.0000e-08\n", "Epoch 302/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7327 - mae: 5.2181 - lr: 1.0000e-08\n", "Epoch 303/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7328 - mae: 5.2182 - lr: 1.0000e-08\n", "Epoch 304/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7327 - mae: 5.2181 - lr: 1.0000e-08\n", "Epoch 305/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7326 - mae: 5.2180 - lr: 1.0000e-08\n", "Epoch 306/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7326 - mae: 5.2180 - lr: 1.0000e-08\n", "Epoch 307/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7326 - mae: 5.2180 - lr: 1.0000e-08\n", "Epoch 308/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-09\n", "Epoch 309/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-09\n", "Epoch 310/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7325 - mae: 5.2180 - lr: 1.0000e-09\n", "Epoch 311/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7325 - mae: 5.2180 - lr: 1.0000e-09\n", "Epoch 312/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7325 - mae: 5.2180 - lr: 1.0000e-09\n", "Epoch 313/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-09\n", "Epoch 314/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-10\n", "Epoch 315/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-10\n", "Epoch 316/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-10\n", "Epoch 317/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-10\n", "Epoch 318/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-10\n", "Epoch 319/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-11\n", "Epoch 320/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-11\n", "Epoch 321/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-11\n", "Epoch 322/600\n", "34/34 [==============================] - 0s 6ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-11\n", "Epoch 323/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-11\n", "Epoch 324/600\n", "34/34 [==============================] - 0s 5ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-12\n", "Epoch 325/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-12\n", "Epoch 326/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-12\n", "Epoch 327/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-12\n", "Epoch 328/600\n", "34/34 [==============================] - 0s 7ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-12\n", "Epoch 329/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-13\n", "Epoch 330/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-13\n", "Epoch 331/600\n", "34/34 [==============================] - 0s 9ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-13\n", "Epoch 332/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-13\n", "Epoch 333/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-13\n", "Epoch 334/600\n", "34/34 [==============================] - 0s 8ms/step - loss: 4.7325 - mae: 5.2179 - lr: 1.0000e-14\n", "Epoch 334: early stopping\n" ] } ], "source": [ "# 建立模型\n", "model_rnn = tf.keras.models.Sequential([\n", " tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n", " input_shape=[window_size]),\n", " tf.keras.layers.SimpleRNN(40, return_sequences=True),\n", " tf.keras.layers.SimpleRNN(40),\n", " tf.keras.layers.Dense(1),\n", " tf.keras.layers.Lambda(lambda x: x * 100.0)\n", "])\n", "\n", "# 設置初始 learning rate\n", "learning_rate = 1.0e-5\n", "\n", "# 設置優化器 \n", "optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n", "\n", "# 編譯模型\n", "model_rnn.compile(loss=tf.keras.losses.Huber(),\n", " optimizer=optimizer,\n", " metrics=[\"mae\"])\n", "\n", "# 訓練模型\n", "history = model_rnn.fit(train_loader, \n", " epochs=600, \n", " callbacks=[\n", " tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', patience=5),\n", " tf.keras.callbacks.EarlyStopping(\n", " monitor='loss',\n", " patience=20,\n", " verbose=2)])" ] }, { "cell_type": "code", "execution_count": 10, "id": "98cec9c1", "metadata": { "id": "98cec9c1", "outputId": "f197e18d-65db-435c-f49a-1e33401da7ad", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "12/12 [==============================] - 1s 8ms/step - loss: 9.7111 - mae: 10.2011\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[9.711051940917969, 10.201111793518066]" ] }, "metadata": {}, "execution_count": 10 } ], "source": [ "model_rnn.evaluate(test_loader)" ] }, { "cell_type": "markdown", "id": "637be4f2", "metadata": { "id": "637be4f2" }, "source": [ "## Model Prediction" ] }, { "cell_type": "code", "execution_count": 11, "id": "c6f4ff31", "metadata": { "id": "c6f4ff31", "outputId": "aa9b31a4-3b4a-4e64-92b0-3082d7ee10d8", "colab": { "base_uri": "https://localhost:8080/", "height": 435 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "12/12 [==============================] - 0s 8ms/step\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "forcast = model_rnn.predict(test_loader)[:, 0]\n", "ground_truth_for_view = series_test[window_size:]\n", "time_for_view = time_test[window_size:]\n", "\n", "plot_series(time_for_view,\n", " [forcast, ground_truth_for_view],\n", " labels=['prediction', 'ground truth'])" ] }, { "cell_type": "code", "execution_count": 12, "id": "6394b58c", "metadata": { "id": "6394b58c", "outputId": "c558ae62-092f-4021-9911-26b92a3ae30f", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8510140026978982" ] }, "metadata": {}, "execution_count": 12 } ], "source": [ "R2(forcast, ground_truth_for_view)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "colab": { "provenance": [], "include_colab_link": true } }, "nbformat": 4, "nbformat_minor": 5 }