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gan.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "GAN_tutorial.ipynb",
"version": "0.3.2",
"views": {},
"default_view": {},
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"metadata": {
"id": "uSHxgiHBdgtO",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "4a0a1365-3d11-4d32-a4c4-1396b1861794",
"executionInfo": {
"status": "ok",
"timestamp": 1527556517332,
"user_tz": -420,
"elapsed": 3492,
"user": {
"displayName": "Nguyen tuan anh",
"photoUrl": "//lh4.googleusercontent.com/-c4nAWdwavCY/AAAAAAAAAAI/AAAAAAAAAAo/jNbEVeV39zE/s50-c-k-no/photo.jpg",
"userId": "108611573643209987894"
}
}
},
"cell_type": "code",
"source": [
"!pip install keras"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (2.1.6)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras) (3.12)\n",
"Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras) (1.14.3)\n",
"Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras) (1.11.0)\n",
"Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras) (0.19.1)\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras) (2.7.1)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "oNnwgEHDdoAR",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "7e73ae1c-b4b4-4a9b-fac4-3b415ec8009f",
"executionInfo": {
"status": "ok",
"timestamp": 1527556653110,
"user_tz": -420,
"elapsed": 14419,
"user": {
"displayName": "Nguyen tuan anh",
"photoUrl": "//lh4.googleusercontent.com/-c4nAWdwavCY/AAAAAAAAAAI/AAAAAAAAAAo/jNbEVeV39zE/s50-c-k-no/photo.jpg",
"userId": "108611573643209987894"
}
}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"import time\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation, Flatten, Reshape\n",
"from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D\n",
"from keras.layers import LeakyReLU, Dropout\n",
"from keras.layers import BatchNormalization\n",
"from keras.optimizers import Adam, RMSprop\n",
"\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "hA4-jwjyd_Yu",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
},
"cell_type": "code",
"source": [
"class ElapsedTimer(object):\n",
" def __init__(self):\n",
" self.start_time = time.time()\n",
" def elapsed(self,sec):\n",
" if sec < 60:\n",
" return str(sec) + \" sec\"\n",
" elif sec < (60 * 60):\n",
" return str(sec / 60) + \" min\"\n",
" else:\n",
" return str(sec / (60 * 60)) + \" hr\"\n",
" def elapsed_time(self):\n",
" print(\"Elapsed: %s \" % self.elapsed(time.time() - self.start_time) )"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ICPMTnkleMcH",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
},
"cell_type": "code",
"source": [
"class DCGAN(object):\n",
" def __init__(self, img_rows=28, img_cols=28, channel=1):\n",
"\n",
" self.img_rows = img_rows\n",
" self.img_cols = img_cols\n",
" self.channel = channel\n",
" self.D = None # discriminator\n",
" self.G = None # generator\n",
" self.AM = None # adversarial model\n",
" self.DM = None # discriminator model\n",
"\n",
" # (W−F+2P)/S+1\n",
" def discriminator(self):\n",
" if self.D:\n",
" return self.D\n",
" self.D = Sequential()\n",
" depth = 64\n",
" dropout = 0.4\n",
" # In: 28 x 28 x 1, depth = 1\n",
" # Out: 14 x 14 x 1, depth=64\n",
" input_shape = (self.img_rows, self.img_cols, self.channel)\n",
" self.D.add(Conv2D(depth*1, 5, strides=2, input_shape=input_shape,\\\n",
" padding='same'))\n",
" self.D.add(LeakyReLU(alpha=0.2))\n",
" self.D.add(Dropout(dropout))\n",
"\n",
" self.D.add(Conv2D(depth*2, 5, strides=2, padding='same'))\n",
" self.D.add(LeakyReLU(alpha=0.2))\n",
" self.D.add(Dropout(dropout))\n",
"\n",
" self.D.add(Conv2D(depth*4, 5, strides=2, padding='same'))\n",
" self.D.add(LeakyReLU(alpha=0.2))\n",
" self.D.add(Dropout(dropout))\n",
"\n",
" self.D.add(Conv2D(depth*8, 5, strides=1, padding='same'))\n",
" self.D.add(LeakyReLU(alpha=0.2))\n",
" self.D.add(Dropout(dropout))\n",
"\n",
" # Out: 1-dim probability\n",
" self.D.add(Flatten())\n",
" self.D.add(Dense(1))\n",
" self.D.add(Activation('sigmoid'))\n",
" self.D.summary()\n",
" return self.D\n",
"\n",
" def generator(self):\n",
" if self.G:\n",
" return self.G\n",
" self.G = Sequential()\n",
" dropout = 0.4\n",
" depth = 64+64+64+64\n",
" dim = 7\n",
" # In: 100\n",
" # Out: dim x dim x depth\n",
" self.G.add(Dense(dim*dim*depth, input_dim=100))\n",
" self.G.add(BatchNormalization(momentum=0.9))\n",
" self.G.add(Activation('relu'))\n",
" self.G.add(Reshape((dim, dim, depth)))\n",
" self.G.add(Dropout(dropout))\n",
"\n",
" # In: dim x dim x depth\n",
" # Out: 2*dim x 2*dim x depth/2\n",
" self.G.add(UpSampling2D())\n",
" self.G.add(Conv2DTranspose(int(depth/2), 5, padding='same'))\n",
" self.G.add(BatchNormalization(momentum=0.9))\n",
" self.G.add(Activation('relu'))\n",
"\n",
" self.G.add(UpSampling2D())\n",
" self.G.add(Conv2DTranspose(int(depth/4), 5, padding='same'))\n",
" self.G.add(BatchNormalization(momentum=0.9))\n",
" self.G.add(Activation('relu'))\n",
"\n",
" self.G.add(Conv2DTranspose(int(depth/8), 5, padding='same'))\n",
" self.G.add(BatchNormalization(momentum=0.9))\n",
" self.G.add(Activation('relu'))\n",
"\n",
" # Out: 28 x 28 x 1 grayscale image [0.0,1.0] per pix\n",
" self.G.add(Conv2DTranspose(1, 5, padding='same'))\n",
" self.G.add(Activation('sigmoid'))\n",
" self.G.summary()\n",
" return self.G\n",
"\n",
" def discriminator_model(self):\n",
" if self.DM:\n",
" return self.DM\n",
" optimizer = RMSprop(lr=0.0002, decay=6e-8)\n",
" self.DM = Sequential()\n",
" self.DM.add(self.discriminator())\n",
" self.DM.compile(loss='binary_crossentropy', optimizer=optimizer,\\\n",
" metrics=['accuracy'])\n",
" return self.DM\n",
"\n",
" def adversarial_model(self):\n",
" if self.AM:\n",
" return self.AM\n",
" optimizer = RMSprop(lr=0.0001, decay=3e-8)\n",
" self.AM = Sequential()\n",
" self.AM.add(self.generator())\n",
" self.AM.add(self.discriminator())\n",
" self.AM.compile(loss='binary_crossentropy', optimizer=optimizer,\\\n",
" metrics=['accuracy'])\n",
" return self.AM\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ENGWae3neXM2",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
},
"cell_type": "code",
"source": [
"class MNIST_DCGAN(object):\n",
" def __init__(self):\n",
" self.img_rows = 28\n",
" self.img_cols = 28\n",
" self.channel = 1\n",
"\n",
" self.x_train = input_data.read_data_sets(\"mnist\",\\\n",
" \tone_hot=True).train.images\n",
" self.x_train = self.x_train.reshape(-1, self.img_rows,\\\n",
" \tself.img_cols, 1).astype(np.float32)\n",
"\n",
" self.DCGAN = DCGAN()\n",
" self.discriminator = self.DCGAN.discriminator_model()\n",
" self.adversarial = self.DCGAN.adversarial_model()\n",
" self.generator = self.DCGAN.generator()\n",
"\n",
" def train(self, train_steps=2000, batch_size=256, save_interval=0):\n",
" noise_input = None\n",
" if save_interval>0:\n",
" noise_input = np.random.uniform(-1.0, 1.0, size=[16, 100])\n",
" for i in range(train_steps):\n",
" images_train = self.x_train[np.random.randint(0,\n",
" self.x_train.shape[0], size=batch_size), :, :, :]\n",
" noise = np.random.uniform(-1.0, 1.0, size=[batch_size, 100])\n",
" images_fake = self.generator.predict(noise)\n",
" x = np.concatenate((images_train, images_fake))\n",
" y = np.ones([2*batch_size, 1])\n",
" y[batch_size:, :] = 0\n",
" d_loss = self.discriminator.train_on_batch(x, y)\n",
"\n",
" y = np.ones([batch_size, 1])\n",
" noise = np.random.uniform(-1.0, 1.0, size=[batch_size, 100])\n",
" a_loss = self.adversarial.train_on_batch(noise, y)\n",
" log_mesg = \"%d: [D loss: %f, acc: %f]\" % (i, d_loss[0], d_loss[1])\n",
" log_mesg = \"%s [A loss: %f, acc: %f]\" % (log_mesg, a_loss[0], a_loss[1])\n",
" print(log_mesg)\n",
" if save_interval>0:\n",
" if (i+1)%save_interval==0:\n",
" self.plot_images(save2file=True, samples=noise_input.shape[0],\\\n",
" noise=noise_input, step=(i+1))\n",
"\n",
" def plot_images(self, save2file=False, fake=True, samples=16, noise=None, step=0):\n",
" filename = 'mnist.png'\n",
" if fake:\n",
" if noise is None:\n",
" noise = np.random.uniform(-1.0, 1.0, size=[samples, 100])\n",
" else:\n",
" filename = \"mnist_%d.png\" % step\n",
" images = self.generator.predict(noise)\n",
" else:\n",
" i = np.random.randint(0, self.x_train.shape[0], samples)\n",
" images = self.x_train[i, :, :, :]\n",
"\n",
" plt.figure(figsize=(10,10))\n",
" for i in range(images.shape[0]):\n",
" plt.subplot(4, 4, i+1)\n",
" image = images[i, :, :, :]\n",
" image = np.reshape(image, [self.img_rows, self.img_cols])\n",
" plt.imshow(image, cmap='gray')\n",
" plt.axis('off')\n",
" plt.tight_layout()\n",
" if save2file:\n",
" plt.savefig(filename)\n",
" plt.close('all')\n",
" else:\n",
" plt.show()"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "3_RlW2Wkerzl",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"base_uri": "https://localhost:8080/",
"height": 172632
},
"outputId": "e789ea8b-add9-4de5-d318-f388c3e1cd6d",
"executionInfo": {
"status": "ok",
"timestamp": 1527562338113,
"user_tz": -420,
"elapsed": 5497375,
"user": {
"displayName": "Nguyen tuan anh",
"photoUrl": "//lh4.googleusercontent.com/-c4nAWdwavCY/AAAAAAAAAAI/AAAAAAAAAAo/jNbEVeV39zE/s50-c-k-no/photo.jpg",
"userId": "108611573643209987894"
}
}
},
"cell_type": "code",
"source": [
"mnist_dcgan = MNIST_DCGAN()\n",
"timer = ElapsedTimer()\n",
"mnist_dcgan.train(train_steps=10000, batch_size=256, save_interval=500)\n",
"timer.elapsed_time()\n",
"mnist_dcgan.plot_images(fake=True)\n",
"mnist_dcgan.plot_images(fake=False, save2file=True)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-4-d9428bd41f87>:7: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please write your own downloading logic.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use urllib or similar directly.\n",
"Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.data to implement this functionality.\n",
"Extracting mnist/train-images-idx3-ubyte.gz\n",
"Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.data to implement this functionality.\n",
"Extracting mnist/train-labels-idx1-ubyte.gz\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.one_hot on tensors.\n",
"Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
"Extracting mnist/t10k-images-idx3-ubyte.gz\n",
"Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
"Extracting mnist/t10k-labels-idx1-ubyte.gz\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_1 (Conv2D) (None, 14, 14, 64) 1664 \n",
"_________________________________________________________________\n",
"leaky_re_lu_1 (LeakyReLU) (None, 14, 14, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 14, 14, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 7, 7, 128) 204928 \n",
"_________________________________________________________________\n",
"leaky_re_lu_2 (LeakyReLU) (None, 7, 7, 128) 0 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 7, 7, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 4, 4, 256) 819456 \n",
"_________________________________________________________________\n",
"leaky_re_lu_3 (LeakyReLU) (None, 4, 4, 256) 0 \n",
"_________________________________________________________________\n",
"dropout_3 (Dropout) (None, 4, 4, 256) 0 \n",
"_________________________________________________________________\n",
"conv2d_4 (Conv2D) (None, 4, 4, 512) 3277312 \n",
"_________________________________________________________________\n",
"leaky_re_lu_4 (LeakyReLU) (None, 4, 4, 512) 0 \n",
"_________________________________________________________________\n",
"dropout_4 (Dropout) (None, 4, 4, 512) 0 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 8192) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 1) 8193 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 1) 0 \n",
"=================================================================\n",
"Total params: 4,311,553\n",
"Trainable params: 4,311,553\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_2 (Dense) (None, 12544) 1266944 \n",
"_________________________________________________________________\n",
"batch_normalization_1 (Batch (None, 12544) 50176 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 12544) 0 \n",
"_________________________________________________________________\n",
"reshape_1 (Reshape) (None, 7, 7, 256) 0 \n",
"_________________________________________________________________\n",
"dropout_5 (Dropout) (None, 7, 7, 256) 0 \n",
"_________________________________________________________________\n",
"up_sampling2d_1 (UpSampling2 (None, 14, 14, 256) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_1 (Conv2DTr (None, 14, 14, 128) 819328 \n",
"_________________________________________________________________\n",
"batch_normalization_2 (Batch (None, 14, 14, 128) 512 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 14, 14, 128) 0 \n",
"_________________________________________________________________\n",
"up_sampling2d_2 (UpSampling2 (None, 28, 28, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_2 (Conv2DTr (None, 28, 28, 64) 204864 \n",
"_________________________________________________________________\n",
"batch_normalization_3 (Batch (None, 28, 28, 64) 256 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 28, 28, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_3 (Conv2DTr (None, 28, 28, 32) 51232 \n",
"_________________________________________________________________\n",
"batch_normalization_4 (Batch (None, 28, 28, 32) 128 \n",
"_________________________________________________________________\n",
"activation_5 (Activation) (None, 28, 28, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_4 (Conv2DTr (None, 28, 28, 1) 801 \n",
"_________________________________________________________________\n",
"activation_6 (Activation) (None, 28, 28, 1) 0 \n",
"=================================================================\n",
"Total params: 2,394,241\n",
"Trainable params: 2,368,705\n",
"Non-trainable params: 25,536\n",
"_________________________________________________________________\n",
"WARNING:tensorflow:Variable *= will be deprecated. Use variable.assign_mul if you want assignment to the variable value or 'x = x * y' if you want a new python Tensor object.\n",
"0: [D loss: 0.691644, acc: 0.529297] [A loss: 1.347496, acc: 0.000000]\n",
"1: [D loss: 0.652509, acc: 0.724609] [A loss: 1.477316, acc: 0.000000]\n",
"2: [D loss: 0.589234, acc: 0.962891] [A loss: 1.234557, acc: 0.000000]\n",
"3: [D loss: 0.830015, acc: 0.500000] [A loss: 2.025546, acc: 0.000000]\n",
"4: [D loss: 0.490596, acc: 0.882812] [A loss: 1.493428, acc: 0.000000]\n",
"5: [D loss: 0.443281, acc: 0.982422] [A loss: 1.505041, acc: 0.000000]\n",
"6: [D loss: 0.404189, acc: 0.986328] [A loss: 1.928179, acc: 0.000000]\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"7: [D loss: 0.421127, acc: 0.929688] [A loss: 2.347838, acc: 0.000000]\n",
"8: [D loss: 0.383633, acc: 0.996094] [A loss: 2.089724, acc: 0.000000]\n",
"9: [D loss: 0.391618, acc: 0.878906] [A loss: 2.977959, acc: 0.000000]\n",
"10: [D loss: 0.372992, acc: 0.990234] [A loss: 1.565651, acc: 0.000000]\n",
"11: [D loss: 0.507475, acc: 0.527344] [A loss: 3.608387, acc: 0.000000]\n",
"12: [D loss: 0.381290, acc: 0.894531] [A loss: 1.754790, acc: 0.000000]\n",
"13: [D loss: 0.299376, acc: 0.996094] [A loss: 2.071305, acc: 0.000000]\n",
"14: [D loss: 0.255700, acc: 0.998047] [A loss: 2.479736, acc: 0.000000]\n",
"15: [D loss: 0.241908, acc: 1.000000] [A loss: 2.793214, acc: 0.000000]\n",
"16: [D loss: 0.215357, acc: 1.000000] [A loss: 2.704420, acc: 0.000000]\n",
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"1916: [D loss: 0.682578, acc: 0.533203] [A loss: 0.843643, acc: 0.238281]\n",
"1917: [D loss: 0.678325, acc: 0.568359] [A loss: 0.837440, acc: 0.265625]\n",
"1918: [D loss: 0.691669, acc: 0.546875] [A loss: 0.932299, acc: 0.132812]\n",
"1919: [D loss: 0.664715, acc: 0.599609] [A loss: 0.818193, acc: 0.304688]\n",
"1920: [D loss: 0.695875, acc: 0.525391] [A loss: 0.920488, acc: 0.136719]\n",
"1921: [D loss: 0.692019, acc: 0.539062] [A loss: 0.837617, acc: 0.253906]\n",
"1922: [D loss: 0.715833, acc: 0.537109] [A loss: 0.907437, acc: 0.199219]\n",
"1923: [D loss: 0.708886, acc: 0.541016] [A loss: 0.975387, acc: 0.121094]\n",
"1924: [D loss: 0.697935, acc: 0.513672] [A loss: 0.773653, acc: 0.347656]\n",
"1925: [D loss: 0.704844, acc: 0.525391] [A loss: 1.036329, acc: 0.093750]\n",
"1926: [D loss: 0.685412, acc: 0.570312] [A loss: 0.749335, acc: 0.417969]\n",
"1927: [D loss: 0.708652, acc: 0.535156] [A loss: 1.001128, acc: 0.113281]\n",
"1928: [D loss: 0.677609, acc: 0.546875] [A loss: 0.732334, acc: 0.406250]\n",
"1929: [D loss: 0.697820, acc: 0.544922] [A loss: 1.055133, acc: 0.078125]\n",
"1930: [D loss: 0.686837, acc: 0.562500] [A loss: 0.658543, acc: 0.597656]\n",
"1931: [D loss: 0.726990, acc: 0.513672] [A loss: 1.035694, acc: 0.070312]\n",
"1932: [D loss: 0.681204, acc: 0.576172] [A loss: 0.708561, acc: 0.496094]\n",
"1933: [D loss: 0.721987, acc: 0.521484] [A loss: 0.950138, acc: 0.121094]\n",
"1934: [D loss: 0.681853, acc: 0.542969] [A loss: 0.749537, acc: 0.421875]\n",
"1935: [D loss: 0.692245, acc: 0.566406] [A loss: 0.845897, acc: 0.214844]\n",
"1936: [D loss: 0.706144, acc: 0.496094] [A loss: 0.836411, acc: 0.257812]\n",
"1937: [D loss: 0.682769, acc: 0.541016] [A loss: 0.806686, acc: 0.292969]\n",
"1938: [D loss: 0.691569, acc: 0.531250] [A loss: 0.835501, acc: 0.230469]\n",
"1939: [D loss: 0.689163, acc: 0.548828] [A loss: 0.819890, acc: 0.257812]\n",
"1940: [D loss: 0.693856, acc: 0.517578] [A loss: 0.931610, acc: 0.148438]\n",
"1941: [D loss: 0.687798, acc: 0.544922] [A loss: 0.804680, acc: 0.285156]\n",
"1942: [D loss: 0.702647, acc: 0.525391] [A loss: 0.907093, acc: 0.140625]\n",
"1943: [D loss: 0.685241, acc: 0.544922] [A loss: 0.817012, acc: 0.250000]\n",
"1944: [D loss: 0.687747, acc: 0.544922] [A loss: 0.882003, acc: 0.179688]\n"
],
"name": "stdout"
},
{
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"text": [
"1945: [D loss: 0.683313, acc: 0.576172] [A loss: 0.839064, acc: 0.250000]\n",
"1946: [D loss: 0.699612, acc: 0.507812] [A loss: 0.884653, acc: 0.207031]\n",
"1947: [D loss: 0.695650, acc: 0.513672] [A loss: 0.801534, acc: 0.296875]\n",
"1948: [D loss: 0.704071, acc: 0.519531] [A loss: 0.950908, acc: 0.093750]\n",
"1949: [D loss: 0.680801, acc: 0.576172] [A loss: 0.760296, acc: 0.335938]\n",
"1950: [D loss: 0.706521, acc: 0.521484] [A loss: 1.012260, acc: 0.078125]\n",
"1951: [D loss: 0.676680, acc: 0.591797] [A loss: 0.729997, acc: 0.421875]\n",
"1952: [D loss: 0.729420, acc: 0.511719] [A loss: 1.064131, acc: 0.046875]\n",
"1953: [D loss: 0.691641, acc: 0.537109] [A loss: 0.660101, acc: 0.593750]\n",
"1954: [D loss: 0.749346, acc: 0.507812] [A loss: 0.961568, acc: 0.117188]\n",
"1955: [D loss: 0.681651, acc: 0.556641] [A loss: 0.789982, acc: 0.273438]\n",
"1956: [D loss: 0.709458, acc: 0.513672] [A loss: 0.911005, acc: 0.144531]\n",
"1957: [D loss: 0.687032, acc: 0.552734] [A loss: 0.797090, acc: 0.285156]\n",
"1958: [D loss: 0.696799, acc: 0.535156] [A loss: 0.851181, acc: 0.214844]\n",
"1959: [D loss: 0.691753, acc: 0.533203] [A loss: 0.844492, acc: 0.222656]\n",
"1960: [D loss: 0.683230, acc: 0.568359] [A loss: 0.831171, acc: 0.234375]\n",
"1961: [D loss: 0.694260, acc: 0.537109] [A loss: 0.839666, acc: 0.222656]\n",
"1962: [D loss: 0.700586, acc: 0.511719] [A loss: 0.802525, acc: 0.308594]\n",
"1963: [D loss: 0.701828, acc: 0.523438] [A loss: 0.893123, acc: 0.183594]\n",
"1964: [D loss: 0.692838, acc: 0.515625] [A loss: 0.721860, acc: 0.460938]\n",
"1965: [D loss: 0.692423, acc: 0.552734] [A loss: 0.951997, acc: 0.109375]\n",
"1966: [D loss: 0.684889, acc: 0.541016] [A loss: 0.756767, acc: 0.375000]\n",
"1967: [D loss: 0.703682, acc: 0.539062] [A loss: 0.939893, acc: 0.121094]\n",
"1968: [D loss: 0.682851, acc: 0.552734] [A loss: 0.754413, acc: 0.425781]\n",
"1969: [D loss: 0.684976, acc: 0.562500] [A loss: 0.974195, acc: 0.097656]\n",
"1970: [D loss: 0.702167, acc: 0.560547] [A loss: 0.766710, acc: 0.394531]\n",
"1971: [D loss: 0.691953, acc: 0.539062] [A loss: 0.949603, acc: 0.113281]\n",
"1972: [D loss: 0.691125, acc: 0.562500] [A loss: 0.749679, acc: 0.398438]\n",
"1973: [D loss: 0.691497, acc: 0.542969] [A loss: 0.902155, acc: 0.156250]\n",
"1974: [D loss: 0.687661, acc: 0.574219] [A loss: 0.828043, acc: 0.273438]\n",
"1975: [D loss: 0.687197, acc: 0.548828] [A loss: 0.866138, acc: 0.160156]\n",
"1976: [D loss: 0.691417, acc: 0.527344] [A loss: 0.919359, acc: 0.125000]\n",
"1977: [D loss: 0.685214, acc: 0.525391] [A loss: 0.794356, acc: 0.300781]\n",
"1978: [D loss: 0.683040, acc: 0.564453] [A loss: 0.943598, acc: 0.109375]\n",
"1979: [D loss: 0.683850, acc: 0.558594] [A loss: 0.826587, acc: 0.289062]\n",
"1980: [D loss: 0.690518, acc: 0.548828] [A loss: 0.900247, acc: 0.191406]\n",
"1981: [D loss: 0.678942, acc: 0.599609] [A loss: 0.778632, acc: 0.378906]\n",
"1982: [D loss: 0.718838, acc: 0.509766] [A loss: 1.010208, acc: 0.078125]\n",
"1983: [D loss: 0.688510, acc: 0.568359] [A loss: 0.702731, acc: 0.484375]\n",
"1984: [D loss: 0.705204, acc: 0.527344] [A loss: 1.025702, acc: 0.082031]\n",
"1985: [D loss: 0.699639, acc: 0.527344] [A loss: 0.773812, acc: 0.328125]\n",
"1986: [D loss: 0.701902, acc: 0.521484] [A loss: 0.918920, acc: 0.156250]\n",
"1987: [D loss: 0.691349, acc: 0.515625] [A loss: 0.783113, acc: 0.335938]\n",
"1988: [D loss: 0.690676, acc: 0.542969] [A loss: 0.857743, acc: 0.214844]\n",
"1989: [D loss: 0.700986, acc: 0.515625] [A loss: 0.845880, acc: 0.277344]\n",
"1990: [D loss: 0.676086, acc: 0.574219] [A loss: 0.846998, acc: 0.246094]\n",
"1991: [D loss: 0.697244, acc: 0.527344] [A loss: 0.882027, acc: 0.175781]\n",
"1992: [D loss: 0.689077, acc: 0.548828] [A loss: 0.803377, acc: 0.273438]\n",
"1993: [D loss: 0.684771, acc: 0.583984] [A loss: 0.913252, acc: 0.160156]\n",
"1994: [D loss: 0.677616, acc: 0.578125] [A loss: 0.816305, acc: 0.265625]\n",
"1995: [D loss: 0.688963, acc: 0.550781] [A loss: 0.959241, acc: 0.152344]\n"
],
"name": "stdout"
},
{
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"text": [
"1996: [D loss: 0.689491, acc: 0.556641] [A loss: 0.783760, acc: 0.308594]\n",
"1997: [D loss: 0.684714, acc: 0.544922] [A loss: 1.008006, acc: 0.085938]\n",
"1998: [D loss: 0.685898, acc: 0.544922] [A loss: 0.786538, acc: 0.312500]\n",
"1999: [D loss: 0.698475, acc: 0.541016] [A loss: 1.008558, acc: 0.078125]\n",
"2000: [D loss: 0.682161, acc: 0.548828] [A loss: 0.695735, acc: 0.515625]\n",
"2001: [D loss: 0.712185, acc: 0.527344] [A loss: 1.056420, acc: 0.042969]\n",
"2002: [D loss: 0.687017, acc: 0.525391] [A loss: 0.677432, acc: 0.539062]\n",
"2003: [D loss: 0.710696, acc: 0.533203] [A loss: 0.954482, acc: 0.121094]\n",
"2004: [D loss: 0.692395, acc: 0.537109] [A loss: 0.734784, acc: 0.410156]\n",
"2005: [D loss: 0.705966, acc: 0.554688] [A loss: 0.941487, acc: 0.101562]\n",
"2006: [D loss: 0.687803, acc: 0.572266] [A loss: 0.782978, acc: 0.343750]\n",
"2007: [D loss: 0.705850, acc: 0.527344] [A loss: 0.951222, acc: 0.109375]\n",
"2008: [D loss: 0.703111, acc: 0.501953] [A loss: 0.800878, acc: 0.312500]\n",
"2009: [D loss: 0.694639, acc: 0.548828] [A loss: 0.920912, acc: 0.113281]\n",
"2010: [D loss: 0.683615, acc: 0.550781] [A loss: 0.802903, acc: 0.332031]\n",
"2011: [D loss: 0.704574, acc: 0.521484] [A loss: 0.923403, acc: 0.136719]\n",
"2012: [D loss: 0.704321, acc: 0.503906] [A loss: 0.775663, acc: 0.363281]\n",
"2013: [D loss: 0.699981, acc: 0.529297] [A loss: 0.957647, acc: 0.125000]\n",
"2014: [D loss: 0.683529, acc: 0.542969] [A loss: 0.757194, acc: 0.410156]\n",
"2015: [D loss: 0.698106, acc: 0.550781] [A loss: 0.932138, acc: 0.167969]\n",
"2016: [D loss: 0.697710, acc: 0.505859] [A loss: 0.733864, acc: 0.429688]\n",
"2017: [D loss: 0.700103, acc: 0.525391] [A loss: 0.957666, acc: 0.113281]\n",
"2018: [D loss: 0.680151, acc: 0.562500] [A loss: 0.739863, acc: 0.460938]\n",
"2019: [D loss: 0.707686, acc: 0.541016] [A loss: 0.969478, acc: 0.089844]\n",
"2020: [D loss: 0.688408, acc: 0.537109] [A loss: 0.696527, acc: 0.496094]\n",
"2021: [D loss: 0.714582, acc: 0.535156] [A loss: 0.964329, acc: 0.101562]\n",
"2022: [D loss: 0.673423, acc: 0.576172] [A loss: 0.714458, acc: 0.476562]\n",
"2023: [D loss: 0.706868, acc: 0.525391] [A loss: 0.977004, acc: 0.082031]\n",
"2024: [D loss: 0.681415, acc: 0.566406] [A loss: 0.752334, acc: 0.398438]\n",
"2025: [D loss: 0.691661, acc: 0.576172] [A loss: 0.940165, acc: 0.109375]\n",
"2026: [D loss: 0.689929, acc: 0.550781] [A loss: 0.803652, acc: 0.269531]\n",
"2027: [D loss: 0.714914, acc: 0.486328] [A loss: 0.884761, acc: 0.218750]\n",
"2028: [D loss: 0.694774, acc: 0.548828] [A loss: 0.880159, acc: 0.156250]\n",
"2029: [D loss: 0.677388, acc: 0.582031] [A loss: 0.815120, acc: 0.289062]\n",
"2030: [D loss: 0.676976, acc: 0.554688] [A loss: 0.873404, acc: 0.199219]\n",
"2031: [D loss: 0.686967, acc: 0.564453] [A loss: 0.857936, acc: 0.203125]\n",
"2032: [D loss: 0.694080, acc: 0.566406] [A loss: 0.848836, acc: 0.246094]\n",
"2033: [D loss: 0.692336, acc: 0.562500] [A loss: 0.920577, acc: 0.136719]\n",
"2034: [D loss: 0.684594, acc: 0.541016] [A loss: 0.836377, acc: 0.226562]\n",
"2035: [D loss: 0.696052, acc: 0.542969] [A loss: 0.886712, acc: 0.199219]\n",
"2036: [D loss: 0.681742, acc: 0.583984] [A loss: 0.875894, acc: 0.171875]\n",
"2037: [D loss: 0.696349, acc: 0.550781] [A loss: 0.917727, acc: 0.171875]\n",
"2038: [D loss: 0.685179, acc: 0.552734] [A loss: 0.858814, acc: 0.226562]\n",
"2039: [D loss: 0.685702, acc: 0.562500] [A loss: 0.861993, acc: 0.218750]\n",
"2040: [D loss: 0.712684, acc: 0.517578] [A loss: 0.955300, acc: 0.121094]\n",
"2041: [D loss: 0.697311, acc: 0.521484] [A loss: 0.808466, acc: 0.292969]\n",
"2042: [D loss: 0.698139, acc: 0.550781] [A loss: 0.975390, acc: 0.105469]\n",
"2043: [D loss: 0.691282, acc: 0.542969] [A loss: 0.760743, acc: 0.355469]\n",
"2044: [D loss: 0.698376, acc: 0.541016] [A loss: 1.068340, acc: 0.054688]\n",
"2045: [D loss: 0.697222, acc: 0.541016] [A loss: 0.643014, acc: 0.617188]\n",
"2046: [D loss: 0.715459, acc: 0.529297] [A loss: 1.034396, acc: 0.078125]\n"
],
"name": "stdout"
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"2047: [D loss: 0.690049, acc: 0.541016] [A loss: 0.724975, acc: 0.425781]\n",
"2048: [D loss: 0.734062, acc: 0.470703] [A loss: 0.890162, acc: 0.187500]\n",
"2049: [D loss: 0.687641, acc: 0.541016] [A loss: 0.830724, acc: 0.246094]\n",
"2050: [D loss: 0.693681, acc: 0.560547] [A loss: 0.780591, acc: 0.355469]\n",
"2051: [D loss: 0.687050, acc: 0.539062] [A loss: 0.886767, acc: 0.187500]\n",
"2052: [D loss: 0.684632, acc: 0.550781] [A loss: 0.810078, acc: 0.250000]\n",
"2053: [D loss: 0.706580, acc: 0.521484] [A loss: 0.907868, acc: 0.171875]\n",
"2054: [D loss: 0.689088, acc: 0.550781] [A loss: 0.797197, acc: 0.289062]\n",
"2055: [D loss: 0.699529, acc: 0.548828] [A loss: 0.854215, acc: 0.222656]\n",
"2056: [D loss: 0.702145, acc: 0.544922] [A loss: 0.868312, acc: 0.203125]\n",
"2057: [D loss: 0.692901, acc: 0.517578] [A loss: 0.838997, acc: 0.238281]\n",
"2058: [D loss: 0.691272, acc: 0.544922] [A loss: 0.903064, acc: 0.140625]\n",
"2059: [D loss: 0.687858, acc: 0.544922] [A loss: 0.780543, acc: 0.375000]\n",
"2060: [D loss: 0.708143, acc: 0.513672] [A loss: 0.919686, acc: 0.152344]\n",
"2061: [D loss: 0.687164, acc: 0.544922] [A loss: 0.832274, acc: 0.257812]\n",
"2062: [D loss: 0.690394, acc: 0.533203] [A loss: 0.917745, acc: 0.132812]\n",
"2063: [D loss: 0.715187, acc: 0.496094] [A loss: 0.774507, acc: 0.351562]\n",
"2064: [D loss: 0.703865, acc: 0.541016] [A loss: 0.920219, acc: 0.183594]\n",
"2065: [D loss: 0.690471, acc: 0.546875] [A loss: 0.838213, acc: 0.253906]\n",
"2066: [D loss: 0.683715, acc: 0.566406] [A loss: 0.771431, acc: 0.332031]\n",
"2067: [D loss: 0.704875, acc: 0.519531] [A loss: 0.938042, acc: 0.089844]\n",
"2068: [D loss: 0.690549, acc: 0.546875] [A loss: 0.779046, acc: 0.300781]\n",
"2069: [D loss: 0.698938, acc: 0.550781] [A loss: 0.939632, acc: 0.132812]\n",
"2070: [D loss: 0.687426, acc: 0.541016] [A loss: 0.758971, acc: 0.386719]\n",
"2071: [D loss: 0.704070, acc: 0.505859] [A loss: 0.988274, acc: 0.074219]\n",
"2072: [D loss: 0.696809, acc: 0.523438] [A loss: 0.680402, acc: 0.539062]\n",
"2073: [D loss: 0.720638, acc: 0.513672] [A loss: 1.074628, acc: 0.054688]\n",
"2074: [D loss: 0.669175, acc: 0.560547] [A loss: 0.685776, acc: 0.554688]\n",
"2075: [D loss: 0.720928, acc: 0.527344] [A loss: 0.886531, acc: 0.187500]\n",
"2076: [D loss: 0.687783, acc: 0.541016] [A loss: 0.836609, acc: 0.250000]\n",
"2077: [D loss: 0.689681, acc: 0.535156] [A loss: 0.850239, acc: 0.222656]\n",
"2078: [D loss: 0.702211, acc: 0.523438] [A loss: 0.902689, acc: 0.132812]\n",
"2079: [D loss: 0.679078, acc: 0.566406] [A loss: 0.786082, acc: 0.332031]\n",
"2080: [D loss: 0.698006, acc: 0.527344] [A loss: 0.836750, acc: 0.246094]\n",
"2081: [D loss: 0.699192, acc: 0.509766] [A loss: 0.914615, acc: 0.164062]\n",
"2082: [D loss: 0.683681, acc: 0.570312] [A loss: 0.806246, acc: 0.277344]\n",
"2083: [D loss: 0.692120, acc: 0.554688] [A loss: 0.973023, acc: 0.097656]\n",
"2084: [D loss: 0.688503, acc: 0.552734] [A loss: 0.819266, acc: 0.320312]\n",
"2085: [D loss: 0.704270, acc: 0.515625] [A loss: 0.911689, acc: 0.207031]\n",
"2086: [D loss: 0.699585, acc: 0.525391] [A loss: 0.786701, acc: 0.320312]\n",
"2087: [D loss: 0.697799, acc: 0.529297] [A loss: 0.854028, acc: 0.207031]\n",
"2088: [D loss: 0.694230, acc: 0.533203] [A loss: 0.808207, acc: 0.273438]\n",
"2089: [D loss: 0.687453, acc: 0.564453] [A loss: 0.907186, acc: 0.160156]\n",
"2090: [D loss: 0.699384, acc: 0.539062] [A loss: 0.907170, acc: 0.152344]\n",
"2091: [D loss: 0.694866, acc: 0.554688] [A loss: 0.825653, acc: 0.253906]\n",
"2092: [D loss: 0.685724, acc: 0.548828] [A loss: 0.857747, acc: 0.167969]\n",
"2093: [D loss: 0.693450, acc: 0.542969] [A loss: 0.842683, acc: 0.210938]\n",
"2094: [D loss: 0.697031, acc: 0.542969] [A loss: 0.856074, acc: 0.246094]\n",
"2095: [D loss: 0.683065, acc: 0.564453] [A loss: 0.845685, acc: 0.253906]\n",
"2096: [D loss: 0.689290, acc: 0.556641] [A loss: 0.894415, acc: 0.183594]\n",
"2097: [D loss: 0.687694, acc: 0.529297] [A loss: 0.780950, acc: 0.347656]\n"
],
"name": "stdout"
},
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"2098: [D loss: 0.714795, acc: 0.515625] [A loss: 0.996111, acc: 0.085938]\n",
"2099: [D loss: 0.684298, acc: 0.546875] [A loss: 0.767728, acc: 0.355469]\n",
"2100: [D loss: 0.703368, acc: 0.535156] [A loss: 0.995337, acc: 0.093750]\n",
"2101: [D loss: 0.693475, acc: 0.531250] [A loss: 0.791064, acc: 0.343750]\n",
"2102: [D loss: 0.696406, acc: 0.537109] [A loss: 0.976625, acc: 0.097656]\n",
"2103: [D loss: 0.695977, acc: 0.535156] [A loss: 0.690524, acc: 0.546875]\n",
"2104: [D loss: 0.717534, acc: 0.517578] [A loss: 1.039950, acc: 0.089844]\n",
"2105: [D loss: 0.687703, acc: 0.531250] [A loss: 0.790781, acc: 0.320312]\n",
"2106: [D loss: 0.684972, acc: 0.548828] [A loss: 0.897465, acc: 0.148438]\n",
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"9421: [D loss: 0.699973, acc: 0.484375] [A loss: 0.722662, acc: 0.449219]\n",
"9422: [D loss: 0.721634, acc: 0.466797] [A loss: 0.903574, acc: 0.070312]\n",
"9423: [D loss: 0.716498, acc: 0.468750] [A loss: 0.669217, acc: 0.593750]\n",
"9424: [D loss: 0.716821, acc: 0.517578] [A loss: 0.864149, acc: 0.164062]\n",
"9425: [D loss: 0.696912, acc: 0.488281] [A loss: 0.721371, acc: 0.464844]\n",
"9426: [D loss: 0.706715, acc: 0.505859] [A loss: 0.861232, acc: 0.144531]\n",
"9427: [D loss: 0.705686, acc: 0.482422] [A loss: 0.726876, acc: 0.425781]\n",
"9428: [D loss: 0.714535, acc: 0.544922] [A loss: 0.854370, acc: 0.152344]\n",
"9429: [D loss: 0.695587, acc: 0.517578] [A loss: 0.758940, acc: 0.363281]\n",
"9430: [D loss: 0.699021, acc: 0.501953] [A loss: 0.853965, acc: 0.191406]\n",
"9431: [D loss: 0.702874, acc: 0.500000] [A loss: 0.770582, acc: 0.375000]\n",
"9432: [D loss: 0.712773, acc: 0.503906] [A loss: 0.799286, acc: 0.277344]\n",
"9433: [D loss: 0.690182, acc: 0.515625] [A loss: 0.768327, acc: 0.347656]\n",
"9434: [D loss: 0.701150, acc: 0.511719] [A loss: 0.782455, acc: 0.328125]\n",
"9435: [D loss: 0.700910, acc: 0.527344] [A loss: 0.788787, acc: 0.285156]\n",
"9436: [D loss: 0.702559, acc: 0.509766] [A loss: 0.780489, acc: 0.304688]\n",
"9437: [D loss: 0.709684, acc: 0.488281] [A loss: 0.784357, acc: 0.285156]\n",
"9438: [D loss: 0.700877, acc: 0.523438] [A loss: 0.842390, acc: 0.199219]\n",
"9439: [D loss: 0.693068, acc: 0.544922] [A loss: 0.835832, acc: 0.203125]\n",
"9440: [D loss: 0.688142, acc: 0.525391] [A loss: 0.806447, acc: 0.246094]\n",
"9441: [D loss: 0.709625, acc: 0.494141] [A loss: 0.892753, acc: 0.097656]\n"
],
"name": "stdout"
},
{
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"9443: [D loss: 0.717986, acc: 0.500000] [A loss: 0.893557, acc: 0.132812]\n",
"9444: [D loss: 0.710859, acc: 0.513672] [A loss: 0.769343, acc: 0.328125]\n",
"9445: [D loss: 0.704774, acc: 0.521484] [A loss: 0.878814, acc: 0.117188]\n",
"9446: [D loss: 0.695501, acc: 0.529297] [A loss: 0.742105, acc: 0.378906]\n",
"9447: [D loss: 0.695186, acc: 0.533203] [A loss: 0.807083, acc: 0.253906]\n",
"9448: [D loss: 0.708195, acc: 0.513672] [A loss: 0.771790, acc: 0.304688]\n",
"9449: [D loss: 0.721568, acc: 0.494141] [A loss: 0.932814, acc: 0.066406]\n",
"9450: [D loss: 0.699999, acc: 0.521484] [A loss: 0.763444, acc: 0.367188]\n",
"9451: [D loss: 0.702397, acc: 0.535156] [A loss: 0.874182, acc: 0.121094]\n",
"9452: [D loss: 0.703972, acc: 0.517578] [A loss: 0.718935, acc: 0.410156]\n",
"9453: [D loss: 0.717513, acc: 0.470703] [A loss: 0.873667, acc: 0.136719]\n",
"9454: [D loss: 0.715531, acc: 0.443359] [A loss: 0.755365, acc: 0.339844]\n",
"9455: [D loss: 0.705315, acc: 0.507812] [A loss: 0.822939, acc: 0.214844]\n",
"9456: [D loss: 0.696745, acc: 0.537109] [A loss: 0.782709, acc: 0.296875]\n",
"9457: [D loss: 0.715364, acc: 0.498047] [A loss: 0.875836, acc: 0.160156]\n",
"9458: [D loss: 0.696779, acc: 0.529297] [A loss: 0.736630, acc: 0.402344]\n",
"9459: [D loss: 0.682910, acc: 0.548828] [A loss: 0.851951, acc: 0.207031]\n",
"9460: [D loss: 0.697112, acc: 0.529297] [A loss: 0.749503, acc: 0.406250]\n",
"9461: [D loss: 0.695100, acc: 0.533203] [A loss: 0.803753, acc: 0.246094]\n",
"9462: [D loss: 0.701876, acc: 0.513672] [A loss: 0.827434, acc: 0.160156]\n",
"9463: [D loss: 0.688269, acc: 0.548828] [A loss: 0.764161, acc: 0.359375]\n",
"9464: [D loss: 0.696198, acc: 0.548828] [A loss: 0.844885, acc: 0.179688]\n",
"9465: [D loss: 0.697195, acc: 0.531250] [A loss: 0.774788, acc: 0.343750]\n",
"9466: [D loss: 0.710932, acc: 0.484375] [A loss: 0.818773, acc: 0.203125]\n",
"9467: [D loss: 0.708511, acc: 0.503906] [A loss: 0.756839, acc: 0.375000]\n",
"9468: [D loss: 0.690324, acc: 0.535156] [A loss: 0.873673, acc: 0.160156]\n",
"9469: [D loss: 0.694266, acc: 0.537109] [A loss: 0.737554, acc: 0.375000]\n",
"9470: [D loss: 0.706014, acc: 0.494141] [A loss: 0.850589, acc: 0.132812]\n",
"9471: [D loss: 0.706717, acc: 0.503906] [A loss: 0.750741, acc: 0.406250]\n",
"9472: [D loss: 0.718960, acc: 0.501953] [A loss: 0.927740, acc: 0.089844]\n",
"9473: [D loss: 0.693879, acc: 0.539062] [A loss: 0.678278, acc: 0.562500]\n",
"9474: [D loss: 0.721467, acc: 0.490234] [A loss: 0.828417, acc: 0.214844]\n",
"9475: [D loss: 0.699778, acc: 0.529297] [A loss: 0.746582, acc: 0.382812]\n",
"9476: [D loss: 0.704542, acc: 0.509766] [A loss: 0.822581, acc: 0.187500]\n",
"9477: [D loss: 0.693797, acc: 0.513672] [A loss: 0.784770, acc: 0.312500]\n",
"9478: [D loss: 0.699212, acc: 0.515625] [A loss: 0.824701, acc: 0.203125]\n",
"9479: [D loss: 0.697221, acc: 0.531250] [A loss: 0.784389, acc: 0.312500]\n",
"9480: [D loss: 0.707716, acc: 0.482422] [A loss: 0.841681, acc: 0.210938]\n",
"9481: [D loss: 0.715902, acc: 0.478516] [A loss: 0.776993, acc: 0.316406]\n",
"9482: [D loss: 0.699143, acc: 0.527344] [A loss: 0.806067, acc: 0.242188]\n",
"9483: [D loss: 0.703579, acc: 0.500000] [A loss: 0.804741, acc: 0.246094]\n",
"9484: [D loss: 0.708049, acc: 0.509766] [A loss: 0.870785, acc: 0.179688]\n",
"9485: [D loss: 0.700276, acc: 0.511719] [A loss: 0.735689, acc: 0.398438]\n",
"9486: [D loss: 0.701577, acc: 0.523438] [A loss: 0.890684, acc: 0.128906]\n",
"9487: [D loss: 0.694468, acc: 0.541016] [A loss: 0.697366, acc: 0.519531]\n",
"9488: [D loss: 0.722440, acc: 0.503906] [A loss: 0.925274, acc: 0.066406]\n",
"9489: [D loss: 0.704031, acc: 0.503906] [A loss: 0.701793, acc: 0.527344]\n",
"9490: [D loss: 0.716613, acc: 0.523438] [A loss: 0.914846, acc: 0.089844]\n",
"9491: [D loss: 0.701683, acc: 0.505859] [A loss: 0.717216, acc: 0.480469]\n",
"9492: [D loss: 0.715254, acc: 0.494141] [A loss: 0.867608, acc: 0.167969]\n"
],
"name": "stdout"
},
{
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"text": [
"9493: [D loss: 0.704450, acc: 0.507812] [A loss: 0.728420, acc: 0.445312]\n",
"9494: [D loss: 0.710740, acc: 0.515625] [A loss: 0.897375, acc: 0.128906]\n",
"9495: [D loss: 0.697659, acc: 0.523438] [A loss: 0.675909, acc: 0.542969]\n",
"9496: [D loss: 0.719921, acc: 0.519531] [A loss: 0.826123, acc: 0.210938]\n",
"9497: [D loss: 0.680664, acc: 0.568359] [A loss: 0.746240, acc: 0.382812]\n",
"9498: [D loss: 0.707859, acc: 0.515625] [A loss: 0.873617, acc: 0.207031]\n",
"9499: [D loss: 0.696889, acc: 0.517578] [A loss: 0.763512, acc: 0.304688]\n",
"9500: [D loss: 0.711182, acc: 0.521484] [A loss: 0.805462, acc: 0.234375]\n",
"9501: [D loss: 0.690703, acc: 0.515625] [A loss: 0.809114, acc: 0.234375]\n",
"9502: [D loss: 0.709943, acc: 0.492188] [A loss: 0.809055, acc: 0.238281]\n",
"9503: [D loss: 0.717363, acc: 0.470703] [A loss: 0.849488, acc: 0.191406]\n",
"9504: [D loss: 0.690007, acc: 0.531250] [A loss: 0.749575, acc: 0.378906]\n",
"9505: [D loss: 0.710609, acc: 0.519531] [A loss: 0.898717, acc: 0.156250]\n",
"9506: [D loss: 0.687754, acc: 0.562500] [A loss: 0.729313, acc: 0.433594]\n",
"9507: [D loss: 0.712994, acc: 0.539062] [A loss: 0.886627, acc: 0.148438]\n",
"9508: [D loss: 0.699554, acc: 0.525391] [A loss: 0.693353, acc: 0.527344]\n",
"9509: [D loss: 0.706329, acc: 0.533203] [A loss: 0.910965, acc: 0.125000]\n",
"9510: [D loss: 0.704521, acc: 0.509766] [A loss: 0.708189, acc: 0.480469]\n",
"9511: [D loss: 0.707647, acc: 0.507812] [A loss: 0.770818, acc: 0.312500]\n",
"9512: [D loss: 0.704925, acc: 0.503906] [A loss: 0.766961, acc: 0.339844]\n",
"9513: [D loss: 0.713390, acc: 0.474609] [A loss: 0.860958, acc: 0.167969]\n",
"9514: [D loss: 0.696824, acc: 0.523438] [A loss: 0.807532, acc: 0.246094]\n",
"9515: [D loss: 0.690577, acc: 0.511719] [A loss: 0.753048, acc: 0.351562]\n",
"9516: [D loss: 0.698833, acc: 0.537109] [A loss: 0.812668, acc: 0.250000]\n",
"9517: [D loss: 0.706093, acc: 0.511719] [A loss: 0.771824, acc: 0.304688]\n",
"9518: [D loss: 0.703484, acc: 0.505859] [A loss: 0.805212, acc: 0.242188]\n",
"9519: [D loss: 0.709326, acc: 0.482422] [A loss: 0.763774, acc: 0.382812]\n",
"9520: [D loss: 0.713175, acc: 0.511719] [A loss: 0.872727, acc: 0.125000]\n",
"9521: [D loss: 0.708818, acc: 0.476562] [A loss: 0.752300, acc: 0.378906]\n",
"9522: [D loss: 0.701967, acc: 0.517578] [A loss: 0.859075, acc: 0.183594]\n",
"9523: [D loss: 0.710141, acc: 0.466797] [A loss: 0.721787, acc: 0.425781]\n",
"9524: [D loss: 0.699804, acc: 0.527344] [A loss: 0.944442, acc: 0.066406]\n",
"9525: [D loss: 0.710440, acc: 0.466797] [A loss: 0.673216, acc: 0.605469]\n",
"9526: [D loss: 0.728608, acc: 0.513672] [A loss: 0.943906, acc: 0.058594]\n
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