{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nimport tensorflow as tf\nimport pandas as pd\nimport tensorflow.keras as keras\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow_addons as tfa\nimport PIL\nimport re\nimport time\n\nfrom kaggle_datasets import KaggleDatasets\nfrom IPython import display\n\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import optimizers\nfrom tensorflow.keras.utils import plot_model\nfrom tensorflow.keras.initializers import RandomNormal\nfrom tensorflow.keras.models import Sequential, Model, load_model\nfrom tensorflow.keras.layers import Conv2D, Conv2DTranspose, Dense, Flatten, Reshape\nfrom tensorflow.keras.layers import BatchNormalization, Dropout\nfrom tensorflow.keras.layers import ReLU, LeakyReLU, Activation\nfrom tensorflow.keras.optimizers import Adam\n\ntry:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print('Device:', tpu.master())\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.TPUStrategy(tpu)\nexcept Exception as e:\n    print(\"can't initialize tpu, using default, exception: \" + str(e))\n    strategy = tf.distribute.get_strategy()\nprint('Number of replicas:', strategy.num_replicas_in_sync)\n\nAUTOTUNE = tf.data.experimental.AUTOTUNE\n\nfrom PIL import Image\nimport shutil\n","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:09.314582Z","iopub.status.busy":"2023-03-01T21:41:09.313778Z","iopub.status.idle":"2023-03-01T21:41:21.207917Z","shell.execute_reply":"2023-03-01T21:41:21.206724Z","shell.execute_reply.started":"2023-03-01T21:41:09.314479Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Part 1: Brief description of the problem and data","metadata":{}},{"cell_type":"markdown","source":"### 1.1 Problem","metadata":{}},{"cell_type":"markdown","source":"A generative adversarial network (GAN) is a generative model that defines an adversarial net framework and is composed of two CNN models, namely a generator and a discriminator, with the goal of generating new realistic images when given a set of training images. These two models act as adversaries of each other: the generator learns to generate new fake images that look like real images (starting with random noise) while the discriminator learns to determine whether a sample image is a real or a fake image. The two models are trained together in a zero-sum game, adversarially, and overtime, the generator gets better at generating images that are super close to real images and discriminator gets better at differentiating them. The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:b92b7788-e5da-4cd5-8be5-11361cbda725.png)\nSource: [Google](https://www.google.com/search?q=what+is+GAN&sxsrf=AJOqlzXFBfOPBLIaIimOQYh8IQgC1mApWQ:1677598187826&source=lnms&tbm=isch&sa=X&ved=2ahUKEwi2oL6gxLj9AhWOm2oFHX1FDSwQ_AUoAnoECAEQBA&biw=2560&bih=1228&dpr=1#imgrc=BcllBc96rDQCgM)\n","metadata":{},"attachments":{"b92b7788-e5da-4cd5-8be5-11361cbda725.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"DCGAN is a type of GAN that uses convolutional neural networks (CNNs) as the generator and discriminator. CNNs are specifically designed for image recognition tasks and are well-suited for generating images using GANs. DCGAN includes several architectural changes compared to a regular GAN. It uses transposed convolutional layers for the generator instead of fully connected layers, and replaces pooling layers with strided convolutions. It also uses batch normalization to stabilize the training process and prevents the generator from collapsing.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:2920c2df-0509-43d6-85d7-3ddbe19a7b2d.png)\nSource: [towardsdatascience.com](https://towardsdatascience.com/cgan-conditional-generative-adversarial-network-how-to-gain-control-over-gan-outputs-b30620bd0cc8)","metadata":{},"attachments":{"2920c2df-0509-43d6-85d7-3ddbe19a7b2d.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"As we can see, the Discriminator model is just a Convolutional classification model. In contrast, the Generator model is more complex as it learns to convert latent inputs into an actual image with the help of Transposed and regular Convolutions. In summary, while both GAN and DCGAN are used for generating new data, DCGAN specifically uses convolutional neural networks as the generator and discriminator, and includes several architectural changes to improve the stability and quality of generated data.","metadata":{}},{"cell_type":"markdown","source":"There are 4 major steps in the training:  \n1. Build the generator.    \n2. Build the discriminator.    \n3. Define Loss Functions & Optimizers.  \n4. Define the training loop & Visualize Images.  ","metadata":{}},{"cell_type":"markdown","source":"In this project, we will build and train a Deep Convolutional Generative Adversarial Network (DCGAN) with Keras to generate images of Monet-style. Then, I will tune DCGAN model by training two more DCGAN models with different hyperparameter and compare the mean loss of these three models to find the best DCGAN model and use it for generating 7000 images of Monet-style. ","metadata":{}},{"cell_type":"markdown","source":"### 1.2 Data\n","metadata":{}},{"cell_type":"markdown","source":"In this project, I use a dataset from Kaggle, was downloaded from the link:  \nhttps://www.kaggle.com/competitions/gan-getting-started/data\n  \nThe dataset contains four directories: monet_tfrec, photo_tfrec, monet_jpg, and photo_jpg. The monet_tfrec and monet_jpg directories contain the same painting images, and the photo_tfrec and photo_jpg   directories contain the same photos.  \n\nThe monet directories contain Monet paintings. We will use these images to train our model.\n\nThe photo directories contain photos. We will add Monet-style to these images and submit our generated jpeg images as a zip file.\n\nFiles   \nmonet_jpg - 300 Monet paintings sized 256x256 in JPEG format   \nmonet_tfrec - 300 Monet paintings sized 256x256 in TFRecord format   \nphoto_jpg - 7028 photos sized 256x256 in JPEG format   \nphoto_tfrec - 7028 photos sized 256x256 in TFRecord format   ","metadata":{}},{"cell_type":"markdown","source":"Reference Sources:    \nhttps://www.kaggle.com/code/amyjang/monet-cyclegan-tutorial/notebook   \nhttps://www.tensorflow.org/tutorials/generative/dcgan   \nhttps://towardsdatascience.com/cgan-conditional-generative-adversarial-network-how-to-gain-control-over-gan-outputs-b30620bd0cc8","metadata":{}},{"cell_type":"markdown","source":"## Part 2: Exploratory Data Analysis (EDA)","metadata":{}},{"cell_type":"markdown","source":"#### Load in the data","metadata":{}},{"cell_type":"markdown","source":"Load in the data by following the [Monet CycleGAN Tutorial](https://www.kaggle.com/code/amyjang/monet-cyclegan-tutorial/notebook).","metadata":{}},{"cell_type":"code","source":"# load in the files of the TFRecords\ngcs_path = KaggleDatasets().get_gcs_path()\n\nmonet_file = tf.io.gfile.glob(str(gcs_path + '/monet_tfrec/*.tfrec'))\nprint('The number of Monet TFRecord Files:', len(monet_file))\n\nphoto_file = tf.io.gfile.glob(str(gcs_path + '/photo_tfrec/*.tfrec'))\nprint('The number of Photo TFRecord Files:', len(photo_file))\n\nnum_monet_samples = np.sum([int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in monet_file])\nprint(f'The number of Monet image files: {num_monet_samples}')\n\nnum_photo_samples = np.sum([int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in photo_file])\nprint(f'The number of Photo image files: {num_photo_samples}')","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:21.210249Z","iopub.status.busy":"2023-03-01T21:41:21.209944Z","iopub.status.idle":"2023-03-01T21:41:21.846963Z","shell.execute_reply":"2023-03-01T21:41:21.845852Z","shell.execute_reply.started":"2023-03-01T21:41:21.210215Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# return the image from the TFRecord\nimage_size = [256, 256]\n\ndef decode_image(image):\n    image = tf.image.decode_jpeg(image, channels=3)\n    image = (tf.cast(image, tf.float32) / 127.5) - 1\n    image = tf.reshape(image, [*image_size, 3])\n    return image\n\ndef read_tfrecord(sample):\n    tfrecord_format = {\n        \"image_name\": tf.io.FixedLenFeature([], tf.string),\n        \"image\": tf.io.FixedLenFeature([], tf.string),\n        \"target\": tf.io.FixedLenFeature([], tf.string)\n    }\n    sample = tf.io.parse_single_example(sample, tfrecord_format)\n    image = decode_image(sample['image'])\n    return image\n","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:21.848863Z","iopub.status.busy":"2023-03-01T21:41:21.848371Z","iopub.status.idle":"2023-03-01T21:41:21.856583Z","shell.execute_reply":"2023-03-01T21:41:21.855521Z","shell.execute_reply.started":"2023-03-01T21:41:21.848825Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# define the function to extract the image from the files\ndef load_data(filenames, labeled=True, ordered=False):\n    data = tf.data.TFRecordDataset(filenames)\n    data = data.map(read_tfrecord, num_parallel_calls=AUTOTUNE)\n    return data","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:21.859045Z","iopub.status.busy":"2023-03-01T21:41:21.858792Z","iopub.status.idle":"2023-03-01T21:41:21.868658Z","shell.execute_reply":"2023-03-01T21:41:21.867651Z","shell.execute_reply.started":"2023-03-01T21:41:21.859015Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load in the datasets\nmonet_ds = load_data(monet_file, labeled=True).batch(32)\nphoto_ds = load_data(photo_file, labeled=True).batch(32)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:21.870604Z","iopub.status.busy":"2023-03-01T21:41:21.870187Z","iopub.status.idle":"2023-03-01T21:41:22.050727Z","shell.execute_reply":"2023-03-01T21:41:22.049745Z","shell.execute_reply.started":"2023-03-01T21:41:21.870554Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create iterators\nsample_monet = next(iter(monet_ds))\nsample_photo = next(iter(photo_ds))","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:22.052566Z","iopub.status.busy":"2023-03-01T21:41:22.052275Z","iopub.status.idle":"2023-03-01T21:41:23.155644Z","shell.execute_reply":"2023-03-01T21:41:23.154489Z","shell.execute_reply.started":"2023-03-01T21:41:22.052532Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# view shape of the datasets\nprint(sample_monet.shape)\nprint(sample_photo.shape)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:23.157601Z","iopub.status.busy":"2023-03-01T21:41:23.157224Z","iopub.status.idle":"2023-03-01T21:41:23.163496Z","shell.execute_reply":"2023-03-01T21:41:23.162517Z","shell.execute_reply.started":"2023-03-01T21:41:23.157547Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# define visualization function to view image\ndef visualize_images(example):\n    plt.figure(figsize = (10, 10))\n    for i in range(25):\n        ax = plt.subplot(5, 5, i + 1)\n        plt.imshow(example[i] * 0.5 + 0.5)\n        plt.axis(\"off\")","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:23.164975Z","iopub.status.busy":"2023-03-01T21:41:23.164660Z","iopub.status.idle":"2023-03-01T21:41:23.174962Z","shell.execute_reply":"2023-03-01T21:41:23.173948Z","shell.execute_reply.started":"2023-03-01T21:41:23.164925Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualize some first images from the monet dataset\nvisualize_images(sample_monet)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:23.176637Z","iopub.status.busy":"2023-03-01T21:41:23.176276Z","iopub.status.idle":"2023-03-01T21:41:25.143957Z","shell.execute_reply":"2023-03-01T21:41:25.143201Z","shell.execute_reply.started":"2023-03-01T21:41:23.176592Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualize some first images from the photo dataset\nvisualize_images(sample_photo)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:25.147033Z","iopub.status.busy":"2023-03-01T21:41:25.146585Z","iopub.status.idle":"2023-03-01T21:41:26.943625Z","shell.execute_reply":"2023-03-01T21:41:26.942674Z","shell.execute_reply.started":"2023-03-01T21:41:25.146980Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Part 3: Building and training Deep Convolutional Generative Adversarial Network (DCGAN)","metadata":{}},{"cell_type":"markdown","source":"DC GAN is one of the most used, powerful, and successful types of GAN architecture. It is implemented with help of ConvNets in place of a Multi-layered perceptron. The ConvNets use a convolutional stride and are built without max pooling and layers in this network are not completely connected.   \n+ Build the generator that takes a noise vector and outputs a tensor of 256x256x3.   \n+ Build the discriminator that takes the tensor of 256x256x3 and outputs a probability that an image is real or fake.   \n+ Create two separate loss functions and optimizers for the generator and discriminator.   ","metadata":{}},{"cell_type":"markdown","source":"#### 3.1 Build the Generator","metadata":{}},{"cell_type":"markdown","source":"The generator network takes random Gaussian noise and maps it into input images such that the discriminator cannot tell which images came from the dataset and which images came from the generator.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:7c9a33a3-9eca-44e0-985a-1c78c8fb2107.png)","metadata":{},"attachments":{"7c9a33a3-9eca-44e0-985a-1c78c8fb2107.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Let’s define our generator model architect:    \nThe generator uses tf.keras.layers.Conv2DTranspose (upsampling) layers to produce an image from a seed (random noise). Start with a Dense layer that takes this seed as input, then upsample several times until we reach the desired image size of 256x256x3. Notice the tf.keras.layers.LeakyReLU activation for each layer, except the output layer which uses tanh.\n\n+ input_shape=(100,).  \n+ Dense is a fully connected layer with a linear activation of size 16x16.  \n+ stride to minimize the spread of the layer outputs, used in place of pooling.  \n+ Five Conv2DTranspose upsampling layers: input goes from 16x16 to 32x32 to 64x64 to 128x128 to 256x256.  \n+ LeakyReLU activation: instead of defining the ReLU activation function as 0 for negative values of inputs(x), we define it as an extremely small linear component of x. \n+ The last Conv2DTranspose upsampling layer is the output layer with tanh activation.  ","metadata":{}},{"cell_type":"code","source":"# create a function to build the generator model\ndef create_generator():\n    model = Sequential(name=\"Generator\")\n    \n    # Hidden Layer 1: Start with 16 x 16 image\n    n_nodes = 16 * 16 * 512 # number of nodes in the first hidden layer\n    model.add(Dense(n_nodes, input_shape=(100,), name='Generator-Hidden-Layer-1'))\n    model.add(Reshape((16, 16, 512), name='Generator-Hidden-Layer-Reshape-1'))\n   \n    # Hidden Layer 2: Upsample to 32 x 32\n    model.add(Conv2DTranspose(filters=256, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator-Hidden-Layer-2'))\n    model.add(LeakyReLU(alpha=0.2))\n        \n    # Hidden Layer 3: Upsample to 64 x 64\n    model.add(Conv2DTranspose(filters=128, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator-Hidden-Layer-3'))\n    model.add(LeakyReLU(alpha=0.2))\n        \n    # Hidden Layer 4: Upsample to 128 x 128\n    model.add(Conv2DTranspose(filters=64, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator-Hidden-Layer-4'))\n    model.add(LeakyReLU(alpha=0.2))\n        \n    # Hidden Layer 5: Upsample to 256 x 256\n    model.add(Conv2DTranspose(filters=32, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator-Hidden-Layer-5'))\n    model.add(LeakyReLU(alpha=0.2))\n        \n    # Output Layer: we use 3 filters because we have 3 channels for a color image.\n    model.add(Conv2DTranspose(3, kernel_size=(3, 3), activation='tanh', strides=(1, 1), padding='same', name='Generator-Output-Layer'))\n  \n    return model","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:26.945050Z","iopub.status.busy":"2023-03-01T21:41:26.944789Z","iopub.status.idle":"2023-03-01T21:41:26.956309Z","shell.execute_reply":"2023-03-01T21:41:26.955331Z","shell.execute_reply.started":"2023-03-01T21:41:26.945017Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Use the noise vector to create an image. The generator is still untrained here!\nwith strategy.scope():\n    generator = create_generator()\n\n# Show model summary\ngenerator.summary()","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:26.957914Z","iopub.status.busy":"2023-03-01T21:41:26.957654Z","iopub.status.idle":"2023-03-01T21:41:27.890455Z","shell.execute_reply":"2023-03-01T21:41:27.889470Z","shell.execute_reply.started":"2023-03-01T21:41:26.957882Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create tmp directory\n!mkdir ../tmp","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:27.891968Z","iopub.status.busy":"2023-03-01T21:41:27.891686Z","iopub.status.idle":"2023-03-01T21:41:28.942228Z","shell.execute_reply":"2023-03-01T21:41:28.940865Z","shell.execute_reply.started":"2023-03-01T21:41:27.891936Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model diagram\nplot_model(generator, to_file=\"../tmp/gen_model.png\", show_shapes=True, show_layer_names=True)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:28.944288Z","iopub.status.busy":"2023-03-01T21:41:28.943958Z","iopub.status.idle":"2023-03-01T21:41:30.312262Z","shell.execute_reply":"2023-03-01T21:41:30.311234Z","shell.execute_reply.started":"2023-03-01T21:41:28.944246Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create vector of random noise to pass through the generator to see what the output is without the network having been trained\nnoise = tf.random.normal([1, 100])\nwith strategy.scope():\n    generated_image = generator(noise, training=False)\n\nplt.imshow(generated_image[0, :, :, 0])","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:30.314251Z","iopub.status.busy":"2023-03-01T21:41:30.313966Z","iopub.status.idle":"2023-03-01T21:41:31.059021Z","shell.execute_reply":"2023-03-01T21:41:31.057758Z","shell.execute_reply.started":"2023-03-01T21:41:30.314213Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 3.2 Build the Discriminator","metadata":{}},{"cell_type":"markdown","source":"The discriminator will be trained to learn to tell the difference between images comes from the dataset and images comes from the generator.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:d1d0ae9a-7e0a-4e0d-98eb-7c01bb91694a.png)","metadata":{},"attachments":{"d1d0ae9a-7e0a-4e0d-98eb-7c01bb91694a.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Let’s now define the model architect:\n\n+ input_shape = [256, 256, 3] \n+ Conv2D downsamples using strides=(2x2)\n+ LeakyReLU activation\n+ Dropout = 0.3: reduces overfitting by reducing the number of neurons.\n+ Flatten to convert each input image into a 1D array: if it receives input data X, it computes X.reshape(-1, 1).  \n+ Dense: output layer with sigmoid activation to return probabilities, a number between 0 and 1, with 1 representing real and 0 representing fake. We use sigmoid because this is a binary classification problem.  \n","metadata":{}},{"cell_type":"code","source":"# create a function to build the discriminator model\ndef create_discriminator():\n    model = Sequential(name=\"Discriminator\") # Model\n    \n    # Hidden Layer 1\n    model.add(Conv2D(filters=32, kernel_size=(3,3), strides=(2, 2), padding='same', input_shape=[256, 256, 3], name='Discriminator-Hidden-Layer-1'))\n    model.add(LeakyReLU(alpha=0.2, name='Discriminator-Hidden-Layer-Activation-1'))\n    \n    # Hidden Layer 2\n    model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(2, 2), padding='same', name='Discriminator-Hidden-Layer-2'))\n    model.add(BatchNormalization())\n    model.add(LeakyReLU(alpha=0.2, name='Discriminator-Hidden-Layer-Activation-2'))\n       \n    # Hidden Layer 3\n    model.add(Conv2D(filters=128, kernel_size=(3,3), strides=(2, 2), padding='same', name='Discriminator-Hidden-Layer-3'))\n    model.add(BatchNormalization())\n    model.add(LeakyReLU(alpha=0.2, name='Discriminator-Hidden-Layer-Activation-3'))\n      \n    # Hidden Layer 4\n    model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(2, 2), padding='same', name='Discriminator-Hidden-Layer-4'))\n    model.add(BatchNormalization())\n    model.add(LeakyReLU(alpha=0.2, name='Discriminator-Hidden-Layer-Activation-4'))\n  \n    # Hidden Layer 5\n    model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(2, 2), padding='same', name='Discriminator-Hidden-Layer-5'))\n    model.add(BatchNormalization())\n    model.add(LeakyReLU(alpha=0.2, name='Discriminator-Hidden-Layer-Activation-5'))\n    \n    # Flatten and Output Layers\n    model.add(Flatten(name='Discriminator-Flatten-Layer')) # Flatten the shape\n    model.add(Dropout(0.3, name='Discriminator-Flatten-Layer-Dropout')) # Randomly drop some connections for better generalization\n    model.add(Dense(1, activation='sigmoid', name='Discriminator-Output-Layer')) # Output Layer\n    \n    return model\n","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:31.060853Z","iopub.status.busy":"2023-03-01T21:41:31.060524Z","iopub.status.idle":"2023-03-01T21:41:31.073038Z","shell.execute_reply":"2023-03-01T21:41:31.072327Z","shell.execute_reply.started":"2023-03-01T21:41:31.060818Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Use the noise vector to create an image. The generator is still untrained here!\nwith strategy.scope():\n    discriminator = create_discriminator()\n\n# Show model summary \ndiscriminator.summary()","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:31.074889Z","iopub.status.busy":"2023-03-01T21:41:31.074592Z","iopub.status.idle":"2023-03-01T21:41:31.775808Z","shell.execute_reply":"2023-03-01T21:41:31.774148Z","shell.execute_reply.started":"2023-03-01T21:41:31.074857Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model diagram\nplot_model(discriminator, to_file=\"../tmp/disc_model.png\", show_shapes=True, show_layer_names=True)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:31.778551Z","iopub.status.busy":"2023-03-01T21:41:31.777665Z","iopub.status.idle":"2023-03-01T21:41:32.089088Z","shell.execute_reply":"2023-03-01T21:41:32.087865Z","shell.execute_reply.started":"2023-03-01T21:41:31.778501Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Use the discriminator to classify the image above (1 for real and 0 for fake)\nwith strategy.scope():\n    decision = discriminator(generated_image)\nprint(decision)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:32.092813Z","iopub.status.busy":"2023-03-01T21:41:32.091745Z","iopub.status.idle":"2023-03-01T21:41:32.193827Z","shell.execute_reply":"2023-03-01T21:41:32.192798Z","shell.execute_reply.started":"2023-03-01T21:41:32.092749Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From the result above, we can see that since the decision is not greater than 0.5 which is closer to 0 so the image is fake.","metadata":{}},{"cell_type":"markdown","source":"### 3.3 Define Loss Functions & Optimizers","metadata":{}},{"cell_type":"markdown","source":"- Generator loss function: the generator's loss quantifies how well it was able to trick the discriminator. Intuitively, if the generator is performing well, the discriminator will classify the fake images as real (or 1). Here, compare the discriminators decisions on the generated images to an array of 1s.  \n- Discriminator loss function: the discriminator's loss quantifies how well the discriminator is able to distinguish real images from fakes. It compares the discriminator's predictions on real images to an array of 1s, and the discriminator's predictions on fake (generated) images to an array of 0s. \n- Both generator and discriminator models use Adam optimizer with learning rate of 0.0002 and beta_1 of 0.5.","metadata":{}},{"cell_type":"code","source":"# create loss function for the generator\nwith strategy.scope():\n    def generator_loss(fake_output):\n        cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)\n        return cross_entropy(tf.ones_like(fake_output), fake_output)\n\n    # create loss function for the discriminator\n    def discriminator_loss(real_output, fake_output):\n        cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)\n        real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n        fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n        total_loss = real_loss + fake_loss\n        return total_loss","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:32.195947Z","iopub.status.busy":"2023-03-01T21:41:32.195568Z","iopub.status.idle":"2023-03-01T21:41:32.204818Z","shell.execute_reply":"2023-03-01T21:41:32.203638Z","shell.execute_reply.started":"2023-03-01T21:41:32.195909Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create two separate optimizers for the generator and discriminator\nwith strategy.scope():\n    generator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)\n    discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:32.207139Z","iopub.status.busy":"2023-03-01T21:41:32.206120Z","iopub.status.idle":"2023-03-01T21:41:32.217928Z","shell.execute_reply":"2023-03-01T21:41:32.216693Z","shell.execute_reply.started":"2023-03-01T21:41:32.207086Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 3.4 Define DCGAN model with the training loop & Visualize Images","metadata":{}},{"cell_type":"markdown","source":"The training loop begins with generator receiving a random noise as input. That noise is used to produce an image. The discriminator is then used to classify real images (drawn from the training set) and fakes images (produced by the generator). The loss is calculated for each of these models, and the gradients are used to update the generator and discriminator.\n\nHere, we create a DCGAN_model which is comprised of:   \n    + train() is a function that performs the training for the generator and discriminator.    \n    + generate_images() is a function to generate images from noise using generator.   \n    + generate_and_plot_images() function generates images from the generator and visualize them.   \n    + train_loop(): finally, we will loop that alternates between training the generator and discriminator for a given number of epochs, print running time and mean loss for every 200 epochs.  \n    \nUsing the tf.function() to improve the performance of TensorFlow code.","metadata":{}},{"cell_type":"code","source":"# Set the hyperparameters to be used for training\nEPOCHS = 1000\nBATCH_SIZE = 32\nnoise_dim = 100\nshape_dim = [256,256,3]","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:32.220272Z","iopub.status.busy":"2023-03-01T21:41:32.219377Z","iopub.status.idle":"2023-03-01T21:41:32.231713Z","shell.execute_reply":"2023-03-01T21:41:32.230587Z","shell.execute_reply.started":"2023-03-01T21:41:32.220200Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class DCGAN_model:\n    def __init__(self, noise_dim, EPOCHS, BATCH_SIZE, generator, discriminator, dataset):  \n        self.noise_dim = noise_dim\n        self.EPOCHS = EPOCHS\n        self.BATCH_SIZE = BATCH_SIZE\n        self.generator = generator\n        #self.discriminator = discriminator2\n        self.dataset = dataset\n    \n    @tf.function\n    def train(self, images):\n    \n    # Create random noise vector\n        noise = tf.random.normal([images.shape[0], noise_dim])\n\n        with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n        \n        # generate images use random noise vector\n            generated_images = self.generator(noise, training=True)\n\n            # use discriminator to evaluate the real and fake images\n            real_output = discriminator(images, training=True)\n            fake_output = discriminator(generated_images, training=True)\n\n            # compute generator loss and discriminator loss\n            gen_loss = generator_loss(fake_output)\n            disc_loss = discriminator_loss(real_output, fake_output)\n\n            # Compute gradients\n            gradients_of_generator = gen_tape.gradient(gen_loss, self.generator.trainable_variables)\n            gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n\n            # Update optimizers\n            generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))\n            discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n        \n        return (gen_loss + disc_loss) * 0.5\n    \n    @tf.function\n    def distributed_train(self, images):\n        per_replica_losses = strategy.run(self.train, args=(images,))\n        return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None)\n    \n    def generate_images(self):\n        noise = tf.random.normal([self.BATCH_SIZE, self.noise_dim]) \n        predictions = self.generator.predict(noise)\n        return predictions\n        \n    def generate_and_plot_images(self):      \n        image = self.generate_images()\n        gen_imgs = 0.5 * image + 0.5\n        fig = plt.figure(figsize=(10, 10))\n        for i in range(25):\n            plt.subplot(5, 5, i+1)\n            plt.imshow(gen_imgs[i, :, :, :])\n            plt.axis('off')\n        plt.show()\n\n    def train_loop(self):\n        e_ls = []\n        mean_ls = []\n        for epoch in range(self.EPOCHS):\n            start = time.time()\n\n            total_loss = 0.0\n            num_batches = 0\n\n            for image_batch in self.dataset:\n                loss = self.distributed_train(image_batch)\n                total_loss += tf.reduce_mean(loss)\n                num_batches += 1\n            mean_loss = total_loss / num_batches\n\n            if (epoch+1) % 200 == 0:                                  \n                print ('Time for epoch {} is {} sec, mean loss is {}'.format(epoch + 1, time.time()-start, mean_loss))\n                self.generate_and_plot_images()\n                \n                e_ls.append(epoch+1)\n                mean_ls.append(mean_loss)\n        print(\"\\nMean Loss for every 200 epochs: \\n\")\n        table = pd.DataFrame({\"Epoch\": e_ls, \"Mean Loss\": np.array(mean_ls)})\n        return table","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:32.234512Z","iopub.status.busy":"2023-03-01T21:41:32.234008Z","iopub.status.idle":"2023-03-01T21:41:32.254778Z","shell.execute_reply":"2023-03-01T21:41:32.253568Z","shell.execute_reply.started":"2023-03-01T21:41:32.234462Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train, visualize and print out the result for DCGAN model\ngan1 = DCGAN_model(noise_dim, EPOCHS, BATCH_SIZE, generator, discriminator, monet_ds)\nres1 = gan1.train_loop()\nres1","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:41:32.256705Z","iopub.status.busy":"2023-03-01T21:41:32.255956Z","iopub.status.idle":"2023-03-01T21:55:43.305789Z","shell.execute_reply":"2023-03-01T21:55:43.304876Z","shell.execute_reply.started":"2023-03-01T21:41:32.256665Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 3.5 Tuning model","metadata":{}},{"cell_type":"markdown","source":"To tune DCGAN model, instead of using LeakyReLU activation: alpha=0.32 as above, now, I would like to create new two generators with LeakyReLU activation: alpha=0.3 and alpha=0.4.","metadata":{}},{"cell_type":"markdown","source":"#### 3.5.1 DCGAN Model 2:","metadata":{}},{"cell_type":"markdown","source":"Architecture:\n+ input_shape=(100,).  \n+ Dense is a fully connected layer with a linear activation of size 16x16.  \n+ stride to minimize the spread of the layer outputs, used in place of pooling.  \n+ Five Conv2DTranspose upsampling layers: input goes from 16x16 to 32x32 to 64x64 to 128x128 to 256x256.  \n+ LeakyReLU activation: alpha=0.3\n+ The last Conv2DTranspose upsampling layer is the output layer with tanh activation.  ","metadata":{}},{"cell_type":"code","source":"# create a function to build the generator model\ndef create_generator2():\n    model = Sequential(name=\"Generator2\")\n    \n    # Hidden Layer 1: Start with 16 x 16 image\n    n_nodes = 16 * 16 * 512 # number of nodes in the first hidden layer\n    model.add(Dense(n_nodes, input_shape=(100,), name='Generator2-Hidden-Layer-1'))\n    model.add(Reshape((16, 16, 512), name='Generator2-Hidden-Layer-Reshape-1'))\n   \n    # Hidden Layer 2: Upsample to 32 x 32\n    model.add(Conv2DTranspose(filters=256, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator2-Hidden-Layer-2'))\n    model.add(LeakyReLU(alpha=0.3))\n        \n    # Hidden Layer 3: Upsample to 64 x 64\n    model.add(Conv2DTranspose(filters=128, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator2-Hidden-Layer-3'))\n    model.add(LeakyReLU(alpha=0.3))\n        \n    # Hidden Layer 4: Upsample to 128 x 128\n    model.add(Conv2DTranspose(filters=64, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator2-Hidden-Layer-4'))\n    model.add(LeakyReLU(alpha=0.3))\n        \n    # Hidden Layer 5: Upsample to 256 x 256\n    model.add(Conv2DTranspose(filters=32, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator2-Hidden-Layer-5'))\n    model.add(LeakyReLU(alpha=0.3))\n        \n    # Output Layer: we use 3 filters because we have 3 channels for a color image.\n    model.add(Conv2DTranspose(3, kernel_size=(3, 3), activation='tanh', strides=(1, 1), padding='same', name='Generator2-Output-Layer'))\n  \n    return model","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:56:00.610478Z","iopub.status.busy":"2023-03-01T21:56:00.609866Z","iopub.status.idle":"2023-03-01T21:56:00.622942Z","shell.execute_reply":"2023-03-01T21:56:00.621877Z","shell.execute_reply.started":"2023-03-01T21:56:00.610438Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Use the noise vector to create an image. The generator is still untrained here!\nwith strategy.scope():\n    generator2 = create_generator2()","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:56:01.251476Z","iopub.status.busy":"2023-03-01T21:56:01.251119Z","iopub.status.idle":"2023-03-01T21:56:01.633383Z","shell.execute_reply":"2023-03-01T21:56:01.632518Z","shell.execute_reply.started":"2023-03-01T21:56:01.251432Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train, visualize and print out the result for DCGAN model 2\ngan2 = DCGAN_model(noise_dim, EPOCHS, BATCH_SIZE, generator2, discriminator, monet_ds)\nres2 = gan2.train_loop()\nres2","metadata":{"execution":{"iopub.execute_input":"2023-03-01T21:56:01.938195Z","iopub.status.busy":"2023-03-01T21:56:01.937382Z","iopub.status.idle":"2023-03-01T22:10:11.563379Z","shell.execute_reply":"2023-03-01T22:10:11.562441Z","shell.execute_reply.started":"2023-03-01T21:56:01.938156Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### 3.5.2 DCGAN Model 3:","metadata":{}},{"cell_type":"markdown","source":"Architecture:\n+ input_shape=(100,).  \n+ Dense is a fully connected layer with a linear activation of size 16x16.  \n+ stride to minimize the spread of the layer outputs, used in place of pooling.  \n+ Five Conv2DTranspose upsampling layers: input goes from 16x16 to 32x32 to 64x64 to 128x128 to 256x256.  \n+ LeakyReLU activation: alpha=0.4.\n+ The last Conv2DTranspose upsampling layer is the output layer with tanh activation.  ","metadata":{}},{"cell_type":"code","source":"# create a function to build the generator model\ndef create_generator3():\n    model = Sequential(name=\"Generator3\")\n    \n    # Hidden Layer 1: Start with 16 x 16 image\n    n_nodes = 16 * 16 * 512 # number of nodes in the first hidden layer\n    model.add(Dense(n_nodes, input_shape=(100,), name='Generator3-Hidden-Layer-1'))\n    model.add(Reshape((16, 16, 512), name='Generator3-Hidden-Layer-Reshape-1'))\n   \n    # Hidden Layer 2: Upsample to 32 x 32\n    model.add(Conv2DTranspose(filters=256, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator3-Hidden-Layer-2'))\n    model.add(LeakyReLU(alpha=0.4))\n        \n    # Hidden Layer 3: Upsample to 64 x 64\n    model.add(Conv2DTranspose(filters=128, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator3-Hidden-Layer-3'))\n    model.add(LeakyReLU(alpha=0.4))\n        \n    # Hidden Layer 4: Upsample to 128 x 128\n    model.add(Conv2DTranspose(filters=64, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator3-Hidden-Layer-4'))\n    model.add(LeakyReLU(alpha=0.4))\n        \n    # Hidden Layer 5: Upsample to 256 x 256\n    model.add(Conv2DTranspose(filters=32, kernel_size=(3, 3), strides=(2, 2), padding='same', name='Generator3-Hidden-Layer-5'))\n    model.add(LeakyReLU(alpha=0.4))\n        \n    # Output Layer: we use 3 filters because we have 3 channels for a color image.\n    model.add(Conv2DTranspose(3, kernel_size=(3, 3), activation='tanh', strides=(1, 1), padding='same', name='Generator3-Output-Layer'))\n  \n    return model","metadata":{"execution":{"iopub.execute_input":"2023-03-01T22:10:45.562542Z","iopub.status.busy":"2023-03-01T22:10:45.562167Z","iopub.status.idle":"2023-03-01T22:10:45.573238Z","shell.execute_reply":"2023-03-01T22:10:45.572141Z","shell.execute_reply.started":"2023-03-01T22:10:45.562503Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Use the noise vector to create an image. The generator is still untrained here!\nwith strategy.scope():\n    generator3 = create_generator3()","metadata":{"execution":{"iopub.execute_input":"2023-03-01T22:10:46.116545Z","iopub.status.busy":"2023-03-01T22:10:46.115481Z","iopub.status.idle":"2023-03-01T22:10:46.504787Z","shell.execute_reply":"2023-03-01T22:10:46.503847Z","shell.execute_reply.started":"2023-03-01T22:10:46.116493Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train, visualize and print out the result for DCGAN model 2\ngan3 = DCGAN_model(noise_dim, EPOCHS, BATCH_SIZE, generator3, discriminator, monet_ds)\nres3 = gan3.train_loop()\nres3","metadata":{"execution":{"iopub.execute_input":"2023-03-01T22:10:46.822075Z","iopub.status.busy":"2023-03-01T22:10:46.821763Z","iopub.status.idle":"2023-03-01T22:24:58.053456Z","shell.execute_reply":"2023-03-01T22:24:58.052513Z","shell.execute_reply.started":"2023-03-01T22:10:46.822043Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Compare three models:","metadata":{}},{"cell_type":"code","source":"# create compare table\ntable = {\"Model\": [\"DCGAN 1\", \"DCGAN 2\", \"DCGAN 3\"],\n        \"Mean_Loss\": [res1[\"Mean Loss\"].mean(), res2[\"Mean Loss\"].mean(), res3[\"Mean Loss\"].mean()]}\ncompare_table = pd.DataFrame(table)\nprint(\"\\nComparing three DCGAN models: \\n\")\ncompare_table","metadata":{"execution":{"iopub.execute_input":"2023-03-01T22:26:19.391185Z","iopub.status.busy":"2023-03-01T22:26:19.390279Z","iopub.status.idle":"2023-03-01T22:26:19.409473Z","shell.execute_reply":"2023-03-01T22:26:19.407918Z","shell.execute_reply.started":"2023-03-01T22:26:19.391145Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Looking at the result above, we con conclude that in this case, DCGAN 1 model with the Generator of LeakyReLU activation: alpha=0.2 has the best performance with the lowest mean loss. Thus, I will use DCGAN 1 to generate 7000 images of Monet-style.","metadata":{}},{"cell_type":"markdown","source":"## Part 4: Submit images","metadata":{}},{"cell_type":"code","source":"# Create new directory\n!mkdir ../images","metadata":{"execution":{"iopub.execute_input":"2023-02-26T15:06:55.925749Z","iopub.status.busy":"2023-02-26T15:06:55.925502Z","iopub.status.idle":"2023-02-26T15:06:57.063320Z","shell.execute_reply":"2023-02-26T15:06:57.061973Z","shell.execute_reply.started":"2023-02-26T15:06:55.925720Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# generate 7000 images\nstart = time.time()\nwith strategy.scope():\n    for i in range(7000):\n        noise = tf.random.normal([BATCH_SIZE, noise_dim])\n        img = generator.predict(noise)\n        img = 0.5 * img + 0.5\n        img = (img * 255).astype('uint8')\n        img = Image.fromarray(img[0, :, :, :])\n        img.save(\"../images/\" + str(i) + \".jpg\")\nprint('Total running time is {} sec'.format(time.time()-start))","metadata":{"execution":{"iopub.execute_input":"2023-02-26T15:06:57.065326Z","iopub.status.busy":"2023-02-26T15:06:57.064926Z","iopub.status.idle":"2023-02-26T15:59:37.201131Z","shell.execute_reply":"2023-02-26T15:59:37.200280Z","shell.execute_reply.started":"2023-02-26T15:06:57.065293Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# view some submission DCGAN_images\nfig = plt.figure(figsize=(10, 10))\nfor i in range(25):\n    plt.subplot(5, 5, i+1)\n    plt.imshow(PIL.Image.open(\"../images/\" + str(i) + \".jpg\"))\n    plt.axis('off')\nplt.show()","metadata":{"execution":{"iopub.execute_input":"2023-02-26T15:59:37.203203Z","iopub.status.busy":"2023-02-26T15:59:37.202895Z","iopub.status.idle":"2023-02-26T15:59:38.570226Z","shell.execute_reply":"2023-02-26T15:59:38.569441Z","shell.execute_reply.started":"2023-02-26T15:59:37.203164Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create a zip file of the images\n#!zip -q -r images.zip ../images\nshutil.make_archive(\"/kaggle/working/images\", 'zip', \"/kaggle/images\")","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2023-02-26T15:59:38.571578Z","iopub.status.busy":"2023-02-26T15:59:38.571367Z","iopub.status.idle":"2023-02-26T15:59:46.016613Z","shell.execute_reply":"2023-02-26T15:59:46.015541Z","shell.execute_reply.started":"2023-02-26T15:59:38.571553Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Part 5: Conclusion and Takeaways","metadata":{}},{"cell_type":"markdown","source":"The goal of this project is to generate 7000 images of monet-style using DCGAN models. There are 5 parts in the report:   \n(1) Brief description of the problem and data    \n(2) Exploratory Data Analysis  \n(3) Building and training DCGAN model  \n(4) Result and Analysis  \n(5) Conclusion and Takeaways   \n\nDCGAN or Deep Convolutional Generative Adversarial Networks is a type of generative model that can learn to generate new images by training on a dataset of existing images. DCGANs have shown impressive results in generating realistic images of faces, animals, landscapes, and other objects. In this project, to generate 7000 images of Monet-style, we build three DCGAN models with three different alpha of LeakyReLU activation included: 0.2, 0.3 and 0.4. By training and comparing mean loss of these three DCGAN models, we got the best model is the DCGAN with LeakyReLU activation: alpha=0.2 since this model has the lowest mean loss.\n\nTraining a DCGAN model requires a significant amount of computational resources and can take a long time, depending on the size of the dataset and the complexity of the model. It is also important to carefully tune the hyperparameters to achieve the best possible results such as:  \n    + add more layers and different types of layers and see the effect on the training time and the stability of the training   \n    + change the number of filters     \n    + adjust the activation functions    \n    + adjust the learning rate: a high learning rate can cause the model to overshoot the optimal weights, while a low learning rate can result in slow convergence.  \n    + add regularization techniques such as dropout, weight decay, or spectral normalization can be used to reduce overfitting and improve the generalization performance of the DCGAN.  \n    ","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}