{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport PIL\nimport time\nfrom skimage.io import imshow\nfrom IPython.display import display\nfrom skimage.transform import resize\nfrom tensorflow.keras.layers import Dense, BatchNormalization, LeakyReLU, Reshape\nfrom tensorflow.keras.layers import Conv2DTranspose, Input, Concatenate, Conv2D\nfrom tensorflow.keras.layers import Conv2D, Dropout, Flatten\nfrom tensorflow.keras.models import Model\nimport os\nimport glob\nfrom tensorflow.keras.callbacks import LambdaCallback\n\n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-06-18T11:01:17.932980Z","iopub.execute_input":"2023-06-18T11:01:17.933531Z","iopub.status.idle":"2023-06-18T11:01:26.600102Z","shell.execute_reply.started":"2023-06-18T11:01:17.933494Z","shell.execute_reply":"2023-06-18T11:01:26.599102Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n  warnings.warn(f\"file system plugins are not loaded: {e}\")\n","output_type":"stream"}]},{"cell_type":"code","source":"def Load_Pprepr_Data():\n    Data = tf.keras.datasets.cifar10.load_data()\n    (train_images , train_labels) , (test_images , test_labels) = Data\n    return train_images ,train_labels ,test_images ,test_labels\n\n\n\ndef create_train_dataset(train_images, train_labels, batch_size):\n    \"\"\"\n    Creates a TensorFlow dataset for training the AC-GAN model.\n\n    Args:\n        train_images (ndarray): Array of training images.\n        train_labels (ndarray): Array of training labels.\n        batch_size (int): Number of samples per batch.\n\n    Returns:\n        tf.data.Dataset: Training dataset.\n    \"\"\"\n\n    # Calculate the buffer size based on the number of training images\n    BUFFER_SIZE = train_images.shape[0]\n    \n    # Create a dataset for the training labels and map them to one-hot encoded vectors\n    train_dataset_y = tf.data.Dataset.from_tensor_slices(train_labels[:, 0]).map(lambda y: tf.one_hot(y, 10))\n    \n    # Create a dataset for the training images\n    train_dataset_x = tf.data.Dataset.from_tensor_slices(train_images)\n    \n    # Combine the image and label datasets, shuffle, and batch them\n    train_dataset = tf.data.Dataset.zip((train_dataset_x, train_dataset_y)).shuffle(BUFFER_SIZE).batch(batch_size)\n    \n    return train_dataset\n\n\n\ndef scale_image_to_float(image):\n    \"\"\"\n    Scales the pixel values of an image to the range [-1, 1].\n\n    Args:\n        image (numpy.ndarray): Input image.\n\n    Returns:\n        numpy.ndarray: Image with pixel values scaled to the range [-1, 1].\n    \"\"\"\n    return (image.astype(np.float32) - 127.5) / 127.5\n\n\ndef scale_image_to_uint8(image):\n    \"\"\"\n    Scales the pixel values of an image to the range [0, 255] and converts them to uint8.\n\n    Args:\n        image (numpy.ndarray): Input image.\n\n    Returns:\n        numpy.ndarray: Image with pixel values scaled to the range [0, 255] and converted to uint8.\n    \"\"\"\n    return np.clip((image * 127.5) + 128, 0, 255).astype(np.uint8)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-06-18T11:01:26.602225Z","iopub.execute_input":"2023-06-18T11:01:26.602964Z","iopub.status.idle":"2023-06-18T11:01:26.615300Z","shell.execute_reply.started":"2023-06-18T11:01:26.602934Z","shell.execute_reply":"2023-06-18T11:01:26.614236Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class AC_GAN:\n\n        \n        \n    def __init__(self):\n        self.generator = None\n        self.discriminator = None\n        self.generator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n        self.discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n        self.generator_gradients = None\n        self.discriminator_gradients = None\n        \n        self.checkpoint_dir = './checkpoints'\n        os.makedirs(self.checkpoint_dir, exist_ok=True)\n        self.checkpoint_prefix = os.path.join(self.checkpoint_dir, 'ckpt')\n\n        # Rest of your code...\n\n        \n        \n        \n    def build_generator(self,latent_dim, num_classes):\n        \"\"\"\n        Builds the AC-GAN generator model.\n\n        Args:\n            latent_dim (int): Dimension of the noise input.\n            num_classes (int): Number of classes/labels.\n\n        Returns:\n            generator (Model): The AC-GAN generator model.\n        \"\"\"\n        # Noise input and label input\n        noise_input = Input((latent_dim,))\n        label_input = Input((num_classes,))\n\n        # Concatenate noise and label inputs\n        x = Concatenate()([noise_input, label_input])\n\n        # Fully connected layer\n        x = Dense(4 * 4 * 256, use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU()(x)\n\n        # Reshape to 4x4x256 feature maps\n        x = Reshape((4, 4, 256))(x)\n\n        # Transposed convolutional layers\n        x = Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU()(x)\n\n        x = Conv2DTranspose(64, (4, 4), strides=(2, 2), padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU()(x)\n\n        x = Conv2DTranspose(32, (4, 4), strides=(2, 2), padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU()(x)\n\n        # Output layer with tanh activation\n        output = Conv2D(3, (3, 3), strides=(1, 1), padding='same', activation='tanh')(x)\n\n        # Create the generator model\n        generator = Model(inputs=[noise_input, label_input], outputs=output)\n\n        return generator\n    \n    \n    \n    \n    \n    def build_discriminator(self,input_shape):\n        \"\"\"\n        Build a discriminator model with the given input shape.\n\n        Parameters:\n            input_shape (tuple): Shape of the input images (height, width, channels).\n\n        Returns:\n            discriminator (Model): Discriminator model.\n\n        \"\"\"\n        _i = Input(input_shape)\n        _ = Conv2D(64, (3, 3), strides=(2, 2), padding='same')(_i)\n        _ = LeakyReLU()(_)  # Apply LeakyReLU activation\n        _ = Conv2D(128, (3, 3), strides=(2, 2), padding='same', use_bias=False)(_)\n        _ = BatchNormalization()(_)  # Apply batch normalization\n        _ = LeakyReLU()(_)  # Apply LeakyReLU activation\n        _ = Conv2D(128, (3, 3), strides=(2, 2), padding='same', use_bias=False)(_)\n        _ = BatchNormalization()(_)  # Apply batch normalization\n        _ = LeakyReLU()(_)  # Apply LeakyReLU activation\n        _ = Conv2D(128, (3, 3), strides=(2, 2), padding='same', use_bias=False)(_)\n        _ = BatchNormalization()(_)  # Apply batch normalization\n        _ = LeakyReLU()(_)  # Apply LeakyReLU activation\n        _ = Flatten()(_)\n        _0 = Dense(1)(_)  # Output for real/fake prediction\n        _1 = Dense(10)(_)  # Output for class prediction\n\n        discriminator = Model(inputs=_i, outputs=[_0, _1])\n        return discriminator\n    \n    def prepare_generation(self,num_generated_examples, latent_dim):\n        \"\"\"\n        Prepare vectors for image generation.\n\n        Args:\n            num_epochs (int): Number of training epochs.\n            num_generated_examples (int): Number of images to generate.\n            latent_dim (int): Dimension of the latent space vector.\n\n        Returns:\n            random_vector_for_generation (tf.Tensor): Random vector for generating images.\n            condition_vector_generation (tf.Tensor): Condition vector for generating images.\n        \"\"\"\n        # Generate random vector for image generation\n        random_vector_for_generation = tf.random.normal([num_generated_examples, latent_dim])\n        condition_vector_generation = tf.one_hot(list(range(10)) * 2, 10)\n\n\n        return random_vector_for_generation, condition_vector_generation\n    \n    \n    \n    def compute_compute_generator_loss(self,generated_output, labels):\n        \"\"\"\n        Computes the generator loss for adversarial and class label matching.\n\n        Args:\n            generated_output (tuple): Tuple containing the discriminator output and class predictions.\n            labels (tf.Tensor): True class labels.\n\n        Returns:\n            tf.Tensor: Total generator loss.\n\n        \"\"\"\n        out_d, out_c = generated_output\n\n        # Calculate the loss component for fooling the discriminator\n        loss_discrit = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n            labels=tf.ones_like(out_d), logits=out_d))\n\n        # Calculate the loss component for matching the desired classes\n        loss_create = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n            labels=labels, logits=out_c))\n\n        # Return the sum of the two loss components\n        whole_loss = loss_discrit + loss_create\n        return whole_loss\n\n\n\n\n\n    def compute_compute_discriminator_loss(self,real_discriminator_output, generated_discriminator_output, true_labels):\n        \"\"\"\n        Computes the discriminator loss for real and generated examples.\n\n        Args:\n            real_discriminator_output (tuple): Tuple containing the discriminator output and class predictions for real examples.\n            generated_discriminator_output (tuple): Tuple containing the discriminator output and class predictions for generated examples.\n            true_labels (tf.Tensor): True class labels.\n\n        Returns:\n            tf.Tensor: Total discriminator loss.\n\n        \"\"\"\n        real_output_d, real_output_c = real_discriminator_output\n\n        # Loss for real examples\n        real_loss_d = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n            labels=tf.ones_like(real_output_d), logits=real_output_d))\n        real_loss_c = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n            labels=true_labels, logits=real_output_c))\n        modified_real_loss = real_loss_d + real_loss_c\n\n        generated_output_d, generated_output_c = generated_discriminator_output\n\n        # Loss for generated examples\n        modified_generated_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n            labels=tf.zeros_like(generated_output_d), logits=generated_output_d))\n\n        # Total loss\n        modified_total_loss = modified_real_loss + modified_generated_loss\n\n        return modified_total_loss\n    \n    \n    \n    \n    def train_model(self,input_images, input_labels,latent_dim,BATCH_SIZE):\n        \"\"\"\n        Performs a single training step for the generator and discriminator models.\n\n        Args:\n            input_images (tf.Tensor): Batch of real images.\n            input_labels (tf.Tensor): Batch of labels for the images.\n\n        Returns:\n            None\n        \"\"\"\n\n        # Generate random noise\n#         noise = tf.random.normal([25, latent_dim])\n        noise = tf.random.normal([BATCH_SIZE, latent_dim])\n#         print('ss')\n#         print(np.array(noise.shape))\n        # Generate random noise\n#         noise = np.random.normal(size=[20, latent_dim])\n\n\n        with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n            # Generate fake images\n            generated_images = self.generator([noise, input_labels], training=True)\n\n            # Get discriminator outputs for real and generated images\n            real_outputs = self.discriminator(input_images, training=True)\n            generated_outputs = self.discriminator(generated_images, training=True)\n\n            # Compute generator and discriminator losses\n            generator_loss = self.compute_compute_generator_loss(generated_outputs, input_labels)\n            discriminator_loss = self.compute_compute_discriminator_loss(real_outputs, generated_outputs, input_labels)\n\n        # Compute gradients for generator and discriminator\n        self.generator_gradients = gen_tape.gradient(generator_loss, self.generator.trainable_variables)\n        self.discriminator_gradients = disc_tape.gradient(discriminator_loss,self.discriminator.trainable_variables)\n\n        # Update generator and discriminator weights\n        self.generator_optimizer.apply_gradients(zip(self.generator_gradients, self.generator.trainable_variables))\n        self.discriminator_optimizer.apply_gradients(zip(self.discriminator_gradients, self.discriminator.trainable_variables))\n\n\n\n    def plot_images(self,images,epoch):\n\n        \n        np.save(\"all_images.npy\", np.array(images))\n        print(f' generated images of epoch {epoch} , saved to all_images.npy')\n        # Create a figure and axes for the subplots with larger figsize\n        fig, axes = plt.subplots(nrows=2, ncols=10, figsize=(40, 20))\n\n        # Flatten the axes array to simplify indexing\n        axes = axes.flatten()\n\n        # Iterate through the images and plot them in the subplots\n        for i in range(20):\n            axes[i].imshow(images[i])\n            axes[i].axis('off')\n\n\n        # Add a title to the entire subplot grid\n        fig.suptitle(f'Generated images of epoch :{epoch}', fontsize=42)\n\n        # Adjust the spacing between subplots\n        plt.tight_layout()\n\n        # Display the plot\n        plt.show()\n\n    def save_weights(self, checkpoint_name):\n        # Delete older weights\n        old_weights = glob.glob(f\"{self.checkpoint_prefix}*\")\n        for weight_file in old_weights:\n            os.remove(weight_file)\n\n        # Save the current weights\n        self.generator.save_weights(f\"{self.checkpoint_prefix}_generator_{checkpoint_name}\")\n        self.discriminator.save_weights(f\"{self.checkpoint_prefix}_discriminator_{checkpoint_name}\")\n\n\n        \n\n        \n        \n        \n\n    def train(self, train_dataset, latent_dim, num_class, num_examples_to_generate, epochs, batch_size=25):\n        self.generator = self.build_generator(latent_dim, num_class)\n        self.discriminator = self.build_discriminator((32, 32, 3))\n\n        random_vector_for_generation, condition_vector_generation = self.prepare_generation(num_examples_to_generate,\n                                                                                           latent_dim)\n        generated = []\n\n        for epoch in range(epochs):\n            # Record the start time of the epoch\n            start_time = time.time()\n\n            # Iterate over the training dataset\n            for images, labels in train_dataset:\n                # Perform a single training step for the generator and discriminator models\n                self.train_model(images, labels, latent_dim, batch_size)\n\n            # Generate random noise\n            noise = np.random.normal(size=[num_examples_to_generate, latent_dim])\n            print(noise.shape)\n\n            # Generate fake images using the generator model\n            generated_image = self.generator([noise, condition_vector_generation], training=False)\n            print(np.array(generated_image).shape)\n\n            self.plot_images(generated_image, epoch)\n            generated.append(generated_image)\n\n            # Print the epoch number and the time taken for the epoch\n            print(epoch, time.time() - start_time)\n\n            self.save_weights(f\"epoch_{epoch}\")\n\n        return np.array(generated)\n\n        \n        \n        \n        \n        \n        \n    \n        \n    \n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-06-18T11:01:26.617236Z","iopub.execute_input":"2023-06-18T11:01:26.617981Z","iopub.status.idle":"2023-06-18T11:01:26.657572Z","shell.execute_reply.started":"2023-06-18T11:01:26.617949Z","shell.execute_reply":"2023-06-18T11:01:26.656509Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"train_images ,train_labels ,test_images ,test_labels = Load_Pprepr_Data()\ntrain_img_f32 = scale_image_to_float(train_images)\ntrain_dataset = create_train_dataset(train_img_f32, train_labels, 25)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-06-18T11:01:26.660332Z","iopub.execute_input":"2023-06-18T11:01:26.660749Z","iopub.status.idle":"2023-06-18T11:01:39.868443Z","shell.execute_reply.started":"2023-06-18T11:01:26.660718Z","shell.execute_reply":"2023-06-18T11:01:39.867491Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n170498071/170498071 [==============================] - 6s 0us/step\n","output_type":"stream"}]},{"cell_type":"code","source":"print('start')\nacgn = AC_GAN()\n\n# Checkpoint directory\ncheckpoint_dir = './checkpoints'\n\n# Check generator and discriminator weights files\ngenerator_weights_file = os.path.join(checkpoint_dir, 'ckpt_generator_epoch_2')\ndiscriminator_weights_file = os.path.join(checkpoint_dir, 'ckpt_discriminator_epoch_2')\n\n# Check if the files exist\ngenerator_files_exist = os.path.exists(generator_weights_file + '.data-00000-of-00001') and \\\n                        os.path.exists(generator_weights_file + '.index')\ndiscriminator_files_exist = os.path.exists(discriminator_weights_file + '.data-00000-of-00001') and \\\n                            os.path.exists(discriminator_weights_file + '.index')\n\n\n\n\nprint(generator_files_exist)\nprint(discriminator_files_exist)\nif(generator_files_exist and discriminator_files_exist):\n    acgn.generator = acgn.build_generator(100, 10)  # Instantiate the generator\n    acgn.discriminator = acgn.build_discriminator((32, 32, 3))\n    acgn.generator.load_weights(generator_weights_file)\n    acgn.discriminator.load_weights(discriminator_weights_file)\n    images = acgn.train(train_dataset,100,10,20,10,25)\nelse:\n    print('no wieght found')\n    images = acgn.train(train_dataset,100,10,20,10,25)\n    \n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-06-18T11:01:39.869860Z","iopub.execute_input":"2023-06-18T11:01:39.870224Z","iopub.status.idle":"2023-06-18T11:36:46.770590Z","shell.execute_reply.started":"2023-06-18T11:01:39.870191Z","shell.execute_reply":"2023-06-18T11:36:46.769647Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"start\nFalse\nFalse\nno wieght found\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 0 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 Axes>","image/png":"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219.799658536911\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 1 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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207.26796746253967\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 2 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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208.18285942077637\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 3 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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211.3893551826477\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 4 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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207.92664694786072\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 6 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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212.2182900905609\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 7 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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209.2111575603485\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 8 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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210.3192059993744\n(20, 100)\n(20, 32, 32, 3)\n generated images of epoch 9 , saved to all_images.npy\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 4000x2000 with 20 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209.35641026496887\n","output_type":"stream"}]},{"cell_type":"code","source":"np.savetxt('images.txt', images, delimiter=',', fmt='%d')","metadata":{"execution":{"iopub.status.busy":"2023-06-18T11:36:46.771861Z","iopub.execute_input":"2023-06-18T11:36:46.772208Z","iopub.status.idle":"2023-06-18T11:36:47.922580Z","shell.execute_reply.started":"2023-06-18T11:36:46.772176Z","shell.execute_reply":"2023-06-18T11:36:47.920564Z"},"trusted":true},"execution_count":6,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavetxt\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mimages.txt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36msavetxt\u001b[0;34m(*args, **kwargs)\u001b[0m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/numpy/lib/npyio.py:1537\u001b[0m, in \u001b[0;36msavetxt\u001b[0;34m(fname, X, fmt, delimiter, newline, header, footer, comments, encoding)\u001b[0m\n\u001b[1;32m   1535\u001b[0m \u001b[38;5;66;03m# Handle 1-dimensional arrays\u001b[39;00m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m-> 1537\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1538\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 1D or 2D array, got \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124mD array instead\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m X\u001b[38;5;241m.\u001b[39mndim)\n\u001b[1;32m   1539\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   1540\u001b[0m     \u001b[38;5;66;03m# Common case -- 1d array of numbers\u001b[39;00m\n\u001b[1;32m   1541\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","\u001b[0;31mValueError\u001b[0m: Expected 1D or 2D array, got 5D array instead"],"ename":"ValueError","evalue":"Expected 1D or 2D array, got 5D array instead","output_type":"error"}]},{"cell_type":"code","source":"images.shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-06-18T11:36:47.923861Z","iopub.status.idle":"2023-06-18T11:36:47.924375Z","shell.execute_reply.started":"2023-06-18T11:36:47.924098Z","shell.execute_reply":"2023-06-18T11:36:47.924120Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.save('array.npy', images)","metadata":{"execution":{"iopub.status.busy":"2023-06-18T11:36:47.925915Z","iopub.status.idle":"2023-06-18T11:36:47.926822Z","shell.execute_reply.started":"2023-06-18T11:36:47.926590Z","shell.execute_reply":"2023-06-18T11:36:47.926613Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a = np.load('all_images.npy')\na.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-18T11:36:47.928141Z","iopub.status.idle":"2023-06-18T11:36:47.929072Z","shell.execute_reply.started":"2023-06-18T11:36:47.928816Z","shell.execute_reply":"2023-06-18T11:36:47.928843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(a.shape[0]):\n    # Get the current image\n    image = a[i]\n\n    # Plot the image\n    plt.figure()\n    plt.imshow(np.transpose(image, (0,1,2)))\n    plt.axis('off')\n    plt.title(f\"Image {i+1}\")\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-18T11:36:47.930595Z","iopub.status.idle":"2023-06-18T11:36:47.931082Z","shell.execute_reply.started":"2023-06-18T11:36:47.930812Z","shell.execute_reply":"2023-06-18T11:36:47.930834Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n\n# Checkpoint directory\ncheckpoint_dir = './checkpoints'\n\n# Check generator and discriminator weights files\ngenerator_weights_file = os.path.join(checkpoint_dir, 'ckpt_generator_epoch_2')\ndiscriminator_weights_file = os.path.join(checkpoint_dir, 'ckpt_discriminator_epoch_2')\n\n# Check if the files exist\ngenerator_files_exist = os.path.exists(generator_weights_file + '.data-00000-of-00001') and \\\n                        os.path.exists(generator_weights_file + '.index')\ndiscriminator_files_exist = os.path.exists(discriminator_weights_file + '.data-00000-of-00001') and \\\n                            os.path.exists(discriminator_weights_file + '.index')\n\n# Print the result\nprint(\"Generator weights exist:\", generator_files_exist)\nprint(\"Discriminator weights exist:\", discriminator_files_exist)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-18T11:36:47.932689Z","iopub.status.idle":"2023-06-18T11:36:47.933141Z","shell.execute_reply.started":"2023-06-18T11:36:47.932906Z","shell.execute_reply":"2023-06-18T11:36:47.932926Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n\n# Checkpoint directory\ncheckpoint_dir = './checkpoints'\n\n# Check generator and discriminator weights files\ngenerator_weights_file = os.path.join(checkpoint_dir, 'ckpt_generator_epoch_2')\ndiscriminator_weights_file = os.path.join(checkpoint_dir, 'ckpt_discriminator_epoch_2')\n\n# Check if the files exist\ngenerator_files_exist = os.path.exists(generator_weights_file + '.data-00000-of-00001') and \\\n                        os.path.exists(generator_weights_file + '.index')\ndiscriminator_files_exist = os.path.exists(discriminator_weights_file + '.data-00000-of-00001') and \\\n                            os.path.exists(discriminator_weights_file + '.index')\n\n# Print the result\nprint(\"Generator weights exist:\", generator_files_exist)\nprint(\"Discriminator weights exist:\", discriminator_files_exist)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-18T11:36:47.940409Z","iopub.status.idle":"2023-06-18T11:36:47.940868Z","shell.execute_reply.started":"2023-06-18T11:36:47.940628Z","shell.execute_reply":"2023-06-18T11:36:47.940649Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def load_weights(generator, discriminator, checkpoint_name):\n    generator_weights_file = f\"ckpt_generator_{checkpoint_name}\"\n    discriminator_weights_file = f\"ckpt_discriminator_{checkpoint_name}\"\n\n    generator_weights_exist = os.path.exists(generator_weights_file + '.data-00000-of-00001') and \\\n                              os.path.exists(generator_weights_file + '.index')\n    discriminator_weights_exist = os.path.exists(discriminator_weights_file + '.data-00000-of-00001') and \\\n                                  os.path.exists(discriminator_weights_file + '.index')\n\n    if generator_weights_exist and discriminator_weights_exist:\n        generator.load_weights(generator_weights_file)\n        discriminator.load_weights(discriminator_weights_file)\n        print(\"Weights loaded successfully.\")\n    else:\n        print(\"No weights found. Starting from scratch.\")\n\n","metadata":{"execution":{"iopub.status.busy":"2023-06-18T11:36:47.942551Z","iopub.status.idle":"2023-06-18T11:36:47.943036Z","shell.execute_reply.started":"2023-06-18T11:36:47.942797Z","shell.execute_reply":"2023-06-18T11:36:47.942819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}