From b0985fe9f151f0f11d057ad7c21c224c9e875444 Mon Sep 17 00:00:00 2001 From: lvmingfu <630944715@qq.com> Date: Wed, 1 Jul 2020 09:27:51 +0800 Subject: [PATCH] Unify code format in notebook --- ...sight_model_lineage_and_data_lineage.ipynb | 78 +++++++++---------- 1 file changed, 35 insertions(+), 43 deletions(-) diff --git a/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb b/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb index 2d98c646..6bd288df 100644 --- a/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb +++ b/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb @@ -100,15 +100,14 @@ "metadata": {}, "outputs": [], "source": [ - "# Network request module, data download module, decompression module\n", "import urllib.request \n", "from urllib.parse import urlparse\n", "import gzip \n", "\n", - "def unzipfile(gzip_path):\n", + "def unzip_file(gzip_path):\n", " \"\"\"unzip dataset file\n", " Args:\n", - " gzip_path: dataset file path\n", + " gzip_path (str): Dataset file path\n", " \"\"\"\n", " open_file = open(gzip_path.replace('.gz',''), 'wb')\n", " gz_file = gzip.GzipFile(gzip_path)\n", @@ -134,7 +133,7 @@ " file_name = os.path.join(train_path,url_parse.path.split('/')[-1])\n", " if not os.path.exists(file_name.replace('.gz', '')):\n", " file = urllib.request.urlretrieve(url, file_name)\n", - " unzipfile(file_name)\n", + " unzip_file(file_name)\n", " os.remove(file_name)\n", " \n", " for url in test_url:\n", @@ -143,7 +142,7 @@ " file_name = os.path.join(test_path,url_parse.path.split('/')[-1])\n", " if not os.path.exists(file_name.replace('.gz', '')):\n", " file = urllib.request.urlretrieve(url, file_name)\n", - " unzipfile(file_name)\n", + " unzip_file(file_name)\n", " os.remove(file_name)\n", "\n", "download_dataset()" @@ -199,36 +198,36 @@ " num_parallel_workers=1):\n", " \"\"\" create dataset for train or test\n", " Args:\n", - " data_path: Data path\n", - " batch_size: The number of data records in each group\n", - " repeat_size: The number of replicated data records\n", - " num_parallel_workers: The number of parallel workers\n", + " data_path (str): Data path\n", + " batch_size (int): The number of data records in each group\n", + " repeat_size (int): The number of replicated data records\n", + " num_parallel_workers (int): The number of parallel workers\n", " \"\"\"\n", " # define dataset\n", " mnist_ds = ds.MnistDataset(data_path)\n", "\n", - " # Define some parameters needed for data enhancement and rough justification\n", + " # define some parameters needed for data enhancement and rough justification\n", " resize_height, resize_width = 32, 32\n", " rescale = 1.0 / 255.0\n", " shift = 0.0\n", " rescale_nml = 1 / 0.3081\n", " shift_nml = -1 * 0.1307 / 0.3081\n", "\n", - " # According to the parameters, generate the corresponding data enhancement method\n", - " resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Resize images to (32, 32) by bilinear interpolation\n", - " rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) # normalize images\n", - " rescale_op = CV.Rescale(rescale, shift) # rescale images\n", - " hwc2chw_op = CV.HWC2CHW() # change shape from (height, width, channel) to (channel, height, width) to fit network.\n", - " type_cast_op = C.TypeCast(mstype.int32) # change data type of label to int32 to fit network\n", + " # according to the parameters, generate the corresponding data enhancement method\n", + " resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)\n", + " rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)\n", + " rescale_op = CV.Rescale(rescale, shift)\n", + " hwc2chw_op = CV.HWC2CHW()\n", + " type_cast_op = C.TypeCast(mstype.int32)\n", "\n", - " # Using map() to apply operations to a dataset\n", + " # using map method to apply operations to a dataset\n", " mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n", " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n", " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n", " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n", " \n", - " # Process the generated dataset\n", + " # process the generated dataset\n", " buffer_size = 10000\n", " mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script\n", " mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)\n", @@ -268,7 +267,6 @@ "import mindspore.nn as nn\n", "from mindspore.common.initializer import TruncatedNormal\n", "\n", - "# Initialize 2D convolution function\n", "def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n", " \"\"\"Conv layer weight initial.\"\"\"\n", " weight = weight_variable()\n", @@ -276,50 +274,46 @@ " kernel_size=kernel_size, stride=stride, padding=padding,\n", " weight_init=weight, has_bias=False, pad_mode=\"valid\")\n", "\n", - "# Initialize full connection layer\n", "def fc_with_initialize(input_channels, out_channels):\n", " \"\"\"Fc layer weight initial.\"\"\"\n", " weight = weight_variable()\n", " bias = weight_variable()\n", " return nn.Dense(input_channels, out_channels, weight, bias)\n", "\n", - "# Set truncated normal distribution\n", "def weight_variable():\n", " \"\"\"Weight initial.\"\"\"\n", " return TruncatedNormal(0.02)\n", "\n", "class LeNet5(nn.Cell):\n", " \"\"\"Lenet network structure.\"\"\"\n", - " # define the operator required\n", " def __init__(self):\n", " super(LeNet5, self).__init__()\n", - " self.batch_size = 32 # 32 pictures in each group\n", - " self.conv1 = conv(1, 6, 5) # Convolution layer 1, 1 channel input (1 Figure), 6 channel output (6 figures), convolution core 5 * 5\n", - " self.conv2 = conv(6, 16, 5) # Convolution layer 2,6-channel input, 16 channel output, convolution kernel 5 * 5\n", + " self.batch_size = 32 \n", + " self.conv1 = conv(1, 6, 5)\n", + " self.conv2 = conv(6, 16, 5)\n", " self.fc1 = fc_with_initialize(16 * 5 * 5, 120)\n", " self.fc2 = fc_with_initialize(120, 84)\n", " self.fc3 = fc_with_initialize(84, 10)\n", " self.relu = nn.ReLU()\n", " self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)\n", " self.flatten = nn.Flatten()\n", - " #Init ImageSummary\n", + " # Init ImageSummary\n", " self.sm_image = P.ImageSummary()\n", "\n", - " # use the preceding operators to construct networks\n", " def construct(self, x):\n", " self.sm_image(\"image\",x)\n", - " x = self.conv1(x) # 1*32*32-->6*28*28\n", - " x = self.relu(x) # 6*28*28-->6*14*14\n", - " x = self.max_pool2d(x) # Pool layer\n", - " x = self.conv2(x) # Convolution layer\n", - " x = self.relu(x) # Function excitation layer\n", - " x = self.max_pool2d(x) # Pool layer\n", - " x = self.flatten(x) # Dimensionality reduction\n", - " x = self.fc1(x) # Full connection\n", - " x = self.relu(x) # Function excitation layer\n", - " x = self.fc2(x) # Full connection\n", - " x = self.relu(x) # Function excitation layer\n", - " x = self.fc3(x) # Full connection\n", + " x = self.conv1(x)\n", + " x = self.relu(x)\n", + " x = self.max_pool2d(x)\n", + " x = self.conv2(x)\n", + " x = self.relu(x)\n", + " x = self.max_pool2d(x)\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.relu(x)\n", + " x = self.fc2(x)\n", + " x = self.relu(x)\n", + " x = self.fc3(x)\n", " return x" ] }, @@ -350,12 +344,10 @@ "metadata": {}, "outputs": [], "source": [ - "# Training and testing related modules\n", "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, SummaryCollector, Callback\n", "from mindspore.train import Model\n", "import os\n", "\n", - "\n", "def train_net(model, epoch_size, mnist_path, repeat_size, ckpoint_cb, summary_collector):\n", " \"\"\"Define the training method.\"\"\"\n", " print(\"============== Starting Training ==============\")\n", @@ -427,7 +419,7 @@ "\n", "if __name__==\"__main__\":\n", " context.set_context(mode=context.GRAPH_MODE, device_target = \"GPU\")\n", - " lr = 0.01 # learning rate\n", + " lr = 0.01\n", " momentum = 0.9 \n", " epoch_size = 3\n", " mnist_path = \"./MNIST_Data\"\n", @@ -618,7 +610,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "浏览器中输入：127.0.0.1:8090连接上MindInsight的服务，点击模型溯源，如下图数据溯源界面：" + "浏览器中输入：`127.0.0.1:8090`连接上MindInsight的服务，点击模型溯源，如下图数据溯源界面：" ] }, { -- GitLab