Owaner/CodeComputeX · Datasets at Hugging Face

code stringlengths 17 6.64M
class Machine(): def __init__(self, max_features=20000, maxlen=80): self.data = Data(max_features, maxlen) self.model = RNN_LSTM(max_features, maxlen) def run(self, epochs=3, batch_size=32): data = self.data model = self.model print('Training stage') print('==...
def main(): m = Machine() m.run()
class AE(models.Model): def __init__(self, x_nodes=784, z_dim=36): x_shape = (x_nodes,) x = layers.Input(shape=x_shape) z = layers.Dense(z_dim, activation='relu')(x) y = layers.Dense(x_nodes, activation='sigmoid')(z) super().__init__(x, y) self.x = x self.z...
def show_ae(autoencoder): encoder = autoencoder.Encoder() decoder = autoencoder.Decoder() encoded_imgs = encoder.predict(x_test) decoded_imgs = decoder.predict(encoded_imgs) n = 10 plt.figure(figsize=(20, 6)) for i in range(n): ax = plt.subplot(3, n, (i + 1)) plt.imshow(x_t...
def main(): x_nodes = 784 z_dim = 36 autoencoder = AE(x_nodes, z_dim) history = autoencoder.fit(x_train, x_train, epochs=10, batch_size=256, shuffle=True, validation_data=(x_test, x_test)) plot_acc(history) plt.show() plot_loss(history) plt.show() show_ae(autoencoder) plt.show(...
def Conv2D(filters, kernel_size, padding='same', activation='relu'): return layers.Conv2D(filters, kernel_size, padding=padding, activation=activation)
class AE(models.Model): def __init__(self, org_shape=(1, 28, 28)): original = layers.Input(shape=org_shape) x = Conv2D(4, (3, 3))(original) x = layers.MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3))(x) x = layers.MaxPooling2D((2, 2), padding='same')(x) ...
def show_ae(autoencoder, data): x_test = data.x_test decoded_imgs = autoencoder.predict(x_test) print(decoded_imgs.shape, data.x_test.shape) if (backend.image_data_format() == 'channels_first'): (N, n_ch, n_i, n_j) = x_test.shape else: (N, n_i, n_j, n_ch) = x_test.shape x_test ...
def main(epochs=20, batch_size=128): data = DATA() autoencoder = AE(data.input_shape) history = autoencoder.fit(data.x_train, data.x_train, epochs=epochs, batch_size=batch_size, shuffle=True, validation_split=0.2) plot_acc(history) plt.show() plot_loss(history) plt.show() show_ae(autoe...
def add_decorate(x): '\n axis = -1 --> last dimension in an array\n ' m = K.mean(x, axis=(- 1), keepdims=True) d = K.square((x - m)) return K.concatenate([x, d], axis=(- 1))
def add_decorate_shape(input_shape): shape = list(input_shape) assert (len(shape) == 2) shape[1] *= 2 return tuple(shape)
def model_compile(model): return model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])
class GAN(): def __init__(self, ni_D, nh_D, nh_G): self.ni_D = ni_D self.nh_D = nh_D self.nh_G = nh_G self.D = self.gen_D() self.G = self.gen_G() self.GD = self.make_GD() def gen_D(self): ni_D = self.ni_D nh_D = self.nh_D D = models.Seq...
class Data(): def __init__(self, mu, sigma, ni_D): self.real_sample = (lambda n_batch: np.random.normal(mu, sigma, (n_batch, ni_D))) self.in_sample = (lambda n_batch: np.random.rand(n_batch, ni_D))
class Machine(): def __init__(self, n_batch=10, ni_D=100): data_mean = 4 data_stddev = 1.25 self.n_iter_D = 1 self.n_iter_G = 5 self.data = Data(data_mean, data_stddev, ni_D) self.gan = GAN(ni_D=ni_D, nh_D=50, nh_G=50) self.n_batch = n_batch def train_...
class GAN_Pure(GAN): def __init__(self, ni_D, nh_D, nh_G): '\n Discriminator input is not added\n ' super().__init__(ni_D, nh_D, nh_G) def gen_D(self): ni_D = self.ni_D nh_D = self.nh_D D = models.Sequential() D.add(Dense(nh_D, activation='relu',...
class Machine_Pure(Machine): def __init__(self, n_batch=10, ni_D=100): data_mean = 4 data_stddev = 1.25 self.data = Data(data_mean, data_stddev, ni_D) self.gan = GAN_Pure(ni_D=ni_D, nh_D=50, nh_G=50) self.n_batch = n_batch
def main(): machine = Machine(n_batch=1, ni_D=100) machine.run(n_repeat=200, n_show=200, n_test=100)
def Conv2D(filters, kernel_size, padding='same', activation='relu'): return layers.Conv2D(filters, kernel_size, padding=padding, activation=activation)
class AE(models.Model): def __init__(self, org_shape=(1, 28, 28)): original = layers.Input(shape=org_shape) x = Conv2D(4, (3, 3))(original) x = layers.MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3))(x) x = layers.MaxPooling2D((2, 2), padding='same')(x) ...
class DATA(): def __init__(self): num_classes = 10 ((x_train, y_train), (x_test, y_test)) = datasets.mnist.load_data() (img_rows, img_cols) = x_train.shape[1:] if (backend.image_data_format() == 'channels_first'): x_train = x_train.reshape(x_train.shape[0], 1, img_rows...
def plot_loss(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show()
class ANN(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = output(h) super().__init__(x, y) self.compile(l...
def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.boston_housing.load_data() scaler = preprocessing.MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) return ((X_train, y_train), (X_test, y_test))
def main(): Nin = 13 Nh = 5 Nout = 1 model = ANN(Nin, Nh, Nout) ((X_train, y_train), (X_test, y_test)) = Data_func() history = model.fit(X_train, y_train, epochs=100, batch_size=100, validation_split=0.2, verbose=2) performace_test = model.evaluate(X_test, y_test, batch_size=100) print...
def ANN_models_func(Nin, Nh, Nout): x = layers.Input(shape=(Nin,)) h = layers.Activation('relu')(layers.Dense(Nh)(x)) y = layers.Activation('softmax')(layers.Dense(Nout)(h)) model = models.Model(x, y) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return...
def ANN_seq_func(Nin, Nh, Nout): model = models.Sequential() model.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) model.add(layers.Dense(Nout, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model
class ANN_models_class(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') softmax = layers.Activation('softmax') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = sof...
class ANN_seq_class(models.Sequential): def __init__(self, Nin, Nh, Nout): super().__init__() self.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) self.add(layers.Dense(Nout, activation='softmax')) self.compile(loss='categorical_crossentropy', optimizer='adam', metric...
def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.mnist.load_data() Y_train = np_utils.to_categorical(y_train) Y_test = np_utils.to_categorical(y_test) (L, W, H) = X_train.shape X_train = X_train.reshape((- 1), (W * H)) X_test = X_test.reshape((- 1), (W * H)) X_train = (X_...
def plot_loss(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model Loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc=0)
def plot_acc(history): plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('Model accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc=0)
def main(): Nin = 784 Nh = 100 number_of_class = 10 Nout = number_of_class model = ANN_seq_class(Nin, Nh, Nout) ((X_train, Y_train), (X_test, Y_test)) = Data_func() history = model.fit(X_train, Y_train, epochs=15, batch_size=100, validation_split=0.2) performace_test = model.evaluate(X...
def plot_acc(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['accuracy']) plt.plot(history['val_accuracy']) if (title is not None): plt.title(title) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Training', 'Veri...
def plot_loss(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['loss']) plt.plot(history['val_loss']) if (title is not None): plt.title(title) plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Training', 'Verification'],...
class History(): def __init__(self): self.history = {'accuracy': [], 'loss': [], 'val_accuracy': [], 'val_loss': []}
class Metrics_Mean(): def __init__(self): self.reset_states() def __call__(self, loss): self.buff.append(loss.data) def reset_states(self): self.buff = [] def result(self): return np.mean(self.buff)
class Metrics_CategoricalAccuracy(): def __init__(self): self.reset_states() def __call__(self, labels, predictions): decisions = predictions.data.max(1)[1] self.correct += decisions.eq(labels.data).cpu().sum() self.L += len(labels.data) def reset_states(self): (...
class ANN_models_class(nn.Module): def __init__(self, Nin, Nh, Nout): super().__init__() self.hidden = nn.Linear(Nin, Nh) self.last = nn.Linear(Nh, Nout) self.Nin = Nin def forward(self, x): x = x.view((- 1), self.Nin) h = F.relu(self.hidden(x)) y = F....
def Data_func(): train_dataset = datasets.MNIST('~/pytorch_data', train=True, download=True, transform=transforms.ToTensor()) test_dataset = datasets.MNIST('~/pytorch_data', train=False, transform=transforms.ToTensor()) train_ds = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, s...
def ANN_models_func(Nin, Nh, Nout): x = layers.Input(shape=(Nin,)) h = layers.Activation('relu')(layers.Dense(Nh)(x)) y = layers.Activation('softmax')(layers.Dense(Nout)(h)) model = models.Model(x, y) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return...
def ANN_seq_func(Nin, Nh, Nout): model = models.Sequential() model.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) model.add(layers.Dense(Nout, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model
class ANN_models_class(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') softmax = layers.Activation('softmax') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = sof...
class ANN_seq_class(models.Sequential): def __init__(self, Nin, Nh, Nout): super().__init__() self.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) self.add(layers.Dense(Nout, activation='softmax')) self.compile(loss='categorical_crossentropy', optimizer='adam', metric...
def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.mnist.load_data() Y_train = utils.to_categorical(y_train) Y_test = utils.to_categorical(y_test) (L, W, H) = X_train.shape X_train = X_train.reshape((- 1), (W * H)) X_test = X_test.reshape((- 1), (W * H)) X_train = (X_train ...
def plot_acc(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['acc']) plt.plot(history['val_acc']) if (title is not None): plt.title(title) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Training', 'Verification']...
def plot_loss(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['loss']) plt.plot(history['val_loss']) if (title is not None): plt.title(title) plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Training', 'Verification'],...
def main(): Nin = 784 Nh = 100 number_of_class = 10 Nout = number_of_class model = ANN_seq_class(Nin, Nh, Nout) ((X_train, Y_train), (X_test, Y_test)) = Data_func() history = model.fit(X_train, Y_train, epochs=15, batch_size=100, validation_split=0.2) performace_test = model.evaluate(X...
def ANN_models_func(Nin, Nh, Nout): x = layers.Input(shape=(Nin,)) h = layers.Activation('relu')(layers.Dense(Nh)(x)) y = layers.Activation('softmax')(layers.Dense(Nout)(h)) model = models.Model(x, y) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return...
def ANN_seq_func(Nin, Nh, Nout): model = models.Sequential() model.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) model.add(layers.Dense(Nout, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model
class ANN_models_class(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') softmax = layers.Activation('softmax') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = sof...
class ANN_seq_class(models.Sequential): def __init__(self, Nin, Nh, Nout): super().__init__() self.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) self.add(layers.Dense(Nout, activation='softmax')) self.compile(loss='categorical_crossentropy', optimizer='adam', metric...
def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.mnist.load_data() Y_train = utils.to_categorical(y_train) Y_test = utils.to_categorical(y_test) (L, W, H) = X_train.shape X_train = X_train.reshape((- 1), (W * H)) X_test = X_test.reshape((- 1), (W * H)) X_train = (X_train ...
def plot_acc(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['accuracy']) plt.plot(history['val_accuracy']) if (title is not None): plt.title(title) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Training', 'Veri...
def plot_loss(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['loss']) plt.plot(history['val_loss']) if (title is not None): plt.title(title) plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Training', 'Verification'],...
class ANN_models_class(models.Model): def __init__(self, Nin, Nh, Nout): super().__init__() self.hidden = layers.Dense(Nh) self.last = layers.Dense(Nout) def call(self, x): relu = layers.Activation('relu') softmax = layers.Activation('softmax') h = relu(self.h...
def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.mnist.load_data() Y_train = utils.to_categorical(y_train) Y_test = utils.to_categorical(y_test) (L, W, H) = X_train.shape X_train = X_train.reshape((- 1), (W * H)) X_test = X_test.reshape((- 1), (W * H)) X_train = (X_train ...
def plot_acc(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['accuracy']) plt.plot(history['val_accuracy']) if (title is not None): plt.title(title) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Training', 'Veri...
def plot_loss(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['loss']) plt.plot(history['val_loss']) if (title is not None): plt.title(title) plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Training', 'Verification'],...
class History(): def __init__(self): self.history = {'accuracy': [], 'loss': [], 'val_accuracy': [], 'val_loss': []}
class _ANN_models_class(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') softmax = layers.Activation('softmax') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = so...
@tf2.function def ep_train(xx, yy): with tf2.GradientTape() as tape: yp = model(xx) loss = Loss_object(yy, yp) gradients = tape.gradient(loss, model.trainable_variables) Optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(loss) train_accuracy(yy, yp)
@tf2.function def ep_test(xx, yy): yp = model(xx) t_loss = Loss_object(yy, yp) test_loss(t_loss) test_accuracy(yy, yp)
class MyModel(Model): def __init__(self): super(MyModel, self).__init__() self.conv1 = Conv2D(32, 3, activation='relu') self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.d2 = Dense(10, activation='softmax') def call(self, x): x = self.conv1...
@tf.function def train_step(images, labels): with tf.GradientTape() as tape: predictions = model(images) loss = loss_object(labels, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(lo...
@tf.function def test_step(images, labels): predictions = model(images) t_loss = loss_object(labels, predictions) test_loss(t_loss) test_accuracy(labels, predictions)
class MultiHeadAttn(nn.Module): def __init__(self, dim_q, dim_k, dim_v, dim_out, num_heads=8): super().__init__() self.num_heads = num_heads self.dim_out = dim_out self.fc_q = nn.Linear(dim_q, dim_out, bias=False) self.fc_k = nn.Linear(dim_k, dim_out, bias=False) s...
class SelfAttn(MultiHeadAttn): def __init__(self, dim_in, dim_out, num_heads=8): super().__init__(dim_in, dim_in, dim_in, dim_out, num_heads) def forward(self, x, mask=None): return super().forward(x, x, x, mask=mask)
def build_mlp(dim_in, dim_hid, dim_out, depth): modules = [nn.Linear(dim_in, dim_hid), nn.ReLU(True)] for _ in range((depth - 2)): modules.append(nn.Linear(dim_hid, dim_hid)) modules.append(nn.ReLU(True)) modules.append(nn.Linear(dim_hid, dim_out)) return nn.Sequential(*modules)
class PoolingEncoder(nn.Module): def __init__(self, dim_x=1, dim_y=1, dim_hid=128, dim_lat=None, self_attn=False, pre_depth=4, post_depth=2): super().__init__() self.use_lat = (dim_lat is not None) self.net_pre = (build_mlp((dim_x + dim_y), dim_hid, dim_hid, pre_depth) if (not self_attn) ...
class CrossAttnEncoder(nn.Module): def __init__(self, dim_x=1, dim_y=1, dim_hid=128, dim_lat=None, self_attn=True, v_depth=4, qk_depth=2): super().__init__() self.use_lat = (dim_lat is not None) if (not self_attn): self.net_v = build_mlp((dim_x + dim_y), dim_hid, dim_hid, v_de...
class Decoder(nn.Module): def __init__(self, dim_x=1, dim_y=1, dim_enc=128, dim_hid=128, depth=3): super().__init__() self.fc = nn.Linear((dim_x + dim_enc), dim_hid) self.dim_hid = dim_hid modules = [nn.ReLU(True)] for _ in range((depth - 2)): modules.append(nn...
def get_logger(filename, mode='a'): logging.basicConfig(level=logging.INFO, format='%(message)s') logger = logging.getLogger() logger.addHandler(logging.FileHandler(filename, mode=mode)) return logger
class RunningAverage(object): def __init__(self, *keys): self.sum = OrderedDict() self.cnt = OrderedDict() self.clock = time.time() for key in keys: self.sum[key] = 0 self.cnt[key] = 0 def update(self, key, val): if isinstance(val, torch.Tensor...
def gen_load_func(parser, func): def load(args, cmdline): (sub_args, cmdline) = parser.parse_known_args(cmdline) for (k, v) in sub_args.__dict__.items(): args.__dict__[k] = v return (func(**sub_args.__dict__), cmdline) return load
def load_module(filename): module_name = os.path.splitext(os.path.basename(filename))[0] return SourceFileLoader(module_name, filename).load_module()
def logmeanexp(x, dim=0): return (x.logsumexp(dim) - math.log(x.shape[dim]))
def stack(x, num_samples=None, dim=0): return (x if (num_samples is None) else torch.stack(([x] * num_samples), dim=dim))
def main(): parser = argparse.ArgumentParser() parser.add_argument('--mode', choices=['train', 'eval', 'plot', 'ensemble'], default='train') parser.add_argument('--expid', type=str, default='trial') parser.add_argument('--resume', action='store_true', default=False) parser.add_argument('--gpu', ty...
def train(args, model): if (not osp.isdir(args.root)): os.makedirs(args.root) with open(osp.join(args.root, 'args.yaml'), 'w') as f: yaml.dump(args.__dict__, f) train_ds = CelebA(train=True) eval_ds = CelebA(train=False) train_loader = torch.utils.data.DataLoader(train_ds, batch_si...
def gen_evalset(args): torch.manual_seed(args.eval_seed) torch.cuda.manual_seed(args.eval_seed) eval_ds = CelebA(train=False) eval_loader = torch.utils.data.DataLoader(eval_ds, batch_size=args.eval_batch_size, shuffle=False, num_workers=4) batches = [] for (x, _) in tqdm(eval_loader): ...
def eval(args, model): if (args.mode == 'eval'): ckpt = torch.load(osp.join(args.root, 'ckpt.tar')) model.load_state_dict(ckpt.model) if (args.eval_logfile is None): eval_logfile = f'eval' if (args.t_noise is not None): eval_logfile += f'_{args.t_noi...
def ensemble(args, model): num_runs = 5 models = [] for i in range(num_runs): model_ = deepcopy(model) ckpt = torch.load(osp.join(results_path, 'celeba', args.model, f'run{(i + 1)}', 'ckpt.tar')) model_.load_state_dict(ckpt['model']) model_.cuda() model_.eval() ...
class CelebA(object): def __init__(self, train=True): (self.data, self.targets) = torch.load(osp.join(datasets_path, 'celeba', ('train.pt' if train else 'eval.pt'))) self.data = (self.data.float() / 255.0) if train: (self.data, self.targets) = (self.data, self.targets) ...
class EMNIST(tvds.EMNIST): def __init__(self, train=True, class_range=[0, 47], device='cpu', download=True): super().__init__(datasets_path, train=train, split='balanced', download=download) self.data = self.data.unsqueeze(1).float().div(255).transpose((- 1), (- 2)).to(device) self.target...
def main(): parser = argparse.ArgumentParser() parser.add_argument('--mode', choices=['train', 'eval', 'plot', 'ensemble'], default='train') parser.add_argument('--expid', type=str, default='trial') parser.add_argument('--resume', action='store_true', default=False) parser.add_argument('--gpu', ty...
def train(args, model): if (not osp.isdir(args.root)): os.makedirs(args.root) with open(osp.join(args.root, 'args.yaml'), 'w') as f: yaml.dump(args.__dict__, f) train_ds = EMNIST(train=True, class_range=args.class_range) eval_ds = EMNIST(train=False, class_range=args.class_range) t...
def gen_evalset(args): torch.manual_seed(args.eval_seed) torch.cuda.manual_seed(args.eval_seed) eval_ds = EMNIST(train=False, class_range=args.class_range) eval_loader = torch.utils.data.DataLoader(eval_ds, batch_size=args.eval_batch_size, shuffle=False, num_workers=4) batches = [] for (x, _) ...
def eval(args, model): if (args.mode == 'eval'): ckpt = torch.load(osp.join(args.root, 'ckpt.tar')) model.load_state_dict(ckpt.model) if (args.eval_logfile is None): (c1, c2) = args.class_range eval_logfile = f'eval_{c1}-{c2}' if (args.t_noise is not Non...
def ensemble(args, model): num_runs = 5 models = [] for i in range(num_runs): model_ = deepcopy(model) ckpt = torch.load(osp.join(results_path, 'emnist', args.model, f'run{(i + 1)}', 'ckpt.tar')) model_.load_state_dict(ckpt['model']) model_.cuda() model_.eval() ...
class MultiHeadAttn(nn.Module): def __init__(self, dim_q, dim_k, dim_v, dim_out, num_heads=8): super().__init__() self.num_heads = num_heads self.dim_out = dim_out self.fc_q = nn.Linear(dim_q, dim_out, bias=False) self.fc_k = nn.Linear(dim_k, dim_out, bias=False) s...
class SelfAttn(MultiHeadAttn): def __init__(self, dim_in, dim_out, num_heads=8): super().__init__(dim_in, dim_in, dim_in, dim_out, num_heads) def forward(self, x, mask=None): return super().forward(x, x, x, mask=mask)
def build_mlp(dim_in, dim_hid, dim_out, depth): modules = [nn.Linear(dim_in, dim_hid), nn.ReLU(True)] for _ in range((depth - 2)): modules.append(nn.Linear(dim_hid, dim_hid)) modules.append(nn.ReLU(True)) modules.append(nn.Linear(dim_hid, dim_out)) return nn.Sequential(*modules)
class PoolingEncoder(nn.Module): def __init__(self, dim_x=1, dim_y=1, dim_hid=128, dim_lat=None, self_attn=False, pre_depth=4, post_depth=2): super().__init__() self.use_lat = (dim_lat is not None) self.net_pre = (build_mlp((dim_x + dim_y), dim_hid, dim_hid, pre_depth) if (not self_attn) ...
class CrossAttnEncoder(nn.Module): def __init__(self, dim_x=1, dim_y=1, dim_hid=128, dim_lat=None, self_attn=True, v_depth=4, qk_depth=2): super().__init__() self.use_lat = (dim_lat is not None) if (not self_attn): self.net_v = build_mlp((dim_x + dim_y), dim_hid, dim_hid, v_de...
class Decoder(nn.Module): def __init__(self, dim_x=1, dim_y=1, dim_enc=128, dim_hid=128, depth=3): super().__init__() self.fc = nn.Linear((dim_x + dim_enc), dim_hid) self.dim_hid = dim_hid modules = [nn.ReLU(True)] for _ in range((depth - 2)): modules.append(nn...
def get_logger(filename, mode='a'): logging.basicConfig(level=logging.INFO, format='%(message)s') logger = logging.getLogger() logger.addHandler(logging.FileHandler(filename, mode=mode)) return logger
class RunningAverage(object): def __init__(self, *keys): self.sum = OrderedDict() self.cnt = OrderedDict() self.clock = time.time() for key in keys: self.sum[key] = 0 self.cnt[key] = 0 def update(self, key, val): if isinstance(val, torch.Tensor...
def gen_load_func(parser, func): def load(args, cmdline): (sub_args, cmdline) = parser.parse_known_args(cmdline) for (k, v) in sub_args.__dict__.items(): args.__dict__[k] = v return (func(**sub_args.__dict__), cmdline) return load
def load_module(filename): module_name = os.path.splitext(os.path.basename(filename))[0] return SourceFileLoader(module_name, filename).load_module()

class Machine(): def __init__(self, max_features=20000, maxlen=80): self.data = Data(max_features, maxlen) self.model = RNN_LSTM(max_features, maxlen) def run(self, epochs=3, batch_size=32): data = self.data model = self.model print('Training stage') print('==...

def main(): m = Machine() m.run()

class AE(models.Model): def __init__(self, x_nodes=784, z_dim=36): x_shape = (x_nodes,) x = layers.Input(shape=x_shape) z = layers.Dense(z_dim, activation='relu')(x) y = layers.Dense(x_nodes, activation='sigmoid')(z) super().__init__(x, y) self.x = x self.z...

def show_ae(autoencoder): encoder = autoencoder.Encoder() decoder = autoencoder.Decoder() encoded_imgs = encoder.predict(x_test) decoded_imgs = decoder.predict(encoded_imgs) n = 10 plt.figure(figsize=(20, 6)) for i in range(n): ax = plt.subplot(3, n, (i + 1)) plt.imshow(x_t...

def main(): x_nodes = 784 z_dim = 36 autoencoder = AE(x_nodes, z_dim) history = autoencoder.fit(x_train, x_train, epochs=10, batch_size=256, shuffle=True, validation_data=(x_test, x_test)) plot_acc(history) plt.show() plot_loss(history) plt.show() show_ae(autoencoder) plt.show(...

def Conv2D(filters, kernel_size, padding='same', activation='relu'): return layers.Conv2D(filters, kernel_size, padding=padding, activation=activation)

class AE(models.Model): def __init__(self, org_shape=(1, 28, 28)): original = layers.Input(shape=org_shape) x = Conv2D(4, (3, 3))(original) x = layers.MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3))(x) x = layers.MaxPooling2D((2, 2), padding='same')(x) ...

def show_ae(autoencoder, data): x_test = data.x_test decoded_imgs = autoencoder.predict(x_test) print(decoded_imgs.shape, data.x_test.shape) if (backend.image_data_format() == 'channels_first'): (N, n_ch, n_i, n_j) = x_test.shape else: (N, n_i, n_j, n_ch) = x_test.shape x_test ...

def main(epochs=20, batch_size=128): data = DATA() autoencoder = AE(data.input_shape) history = autoencoder.fit(data.x_train, data.x_train, epochs=epochs, batch_size=batch_size, shuffle=True, validation_split=0.2) plot_acc(history) plt.show() plot_loss(history) plt.show() show_ae(autoe...

def add_decorate(x): '\n axis = -1 --> last dimension in an array\n ' m = K.mean(x, axis=(- 1), keepdims=True) d = K.square((x - m)) return K.concatenate([x, d], axis=(- 1))

def add_decorate_shape(input_shape): shape = list(input_shape) assert (len(shape) == 2) shape[1] *= 2 return tuple(shape)

def model_compile(model): return model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])

class GAN(): def __init__(self, ni_D, nh_D, nh_G): self.ni_D = ni_D self.nh_D = nh_D self.nh_G = nh_G self.D = self.gen_D() self.G = self.gen_G() self.GD = self.make_GD() def gen_D(self): ni_D = self.ni_D nh_D = self.nh_D D = models.Seq...

class Data(): def __init__(self, mu, sigma, ni_D): self.real_sample = (lambda n_batch: np.random.normal(mu, sigma, (n_batch, ni_D))) self.in_sample = (lambda n_batch: np.random.rand(n_batch, ni_D))

class Machine(): def __init__(self, n_batch=10, ni_D=100): data_mean = 4 data_stddev = 1.25 self.n_iter_D = 1 self.n_iter_G = 5 self.data = Data(data_mean, data_stddev, ni_D) self.gan = GAN(ni_D=ni_D, nh_D=50, nh_G=50) self.n_batch = n_batch def train_...

class GAN_Pure(GAN): def __init__(self, ni_D, nh_D, nh_G): '\n Discriminator input is not added\n ' super().__init__(ni_D, nh_D, nh_G) def gen_D(self): ni_D = self.ni_D nh_D = self.nh_D D = models.Sequential() D.add(Dense(nh_D, activation='relu',...

class Machine_Pure(Machine): def __init__(self, n_batch=10, ni_D=100): data_mean = 4 data_stddev = 1.25 self.data = Data(data_mean, data_stddev, ni_D) self.gan = GAN_Pure(ni_D=ni_D, nh_D=50, nh_G=50) self.n_batch = n_batch

def main(): machine = Machine(n_batch=1, ni_D=100) machine.run(n_repeat=200, n_show=200, n_test=100)

def Conv2D(filters, kernel_size, padding='same', activation='relu'): return layers.Conv2D(filters, kernel_size, padding=padding, activation=activation)

class AE(models.Model): def __init__(self, org_shape=(1, 28, 28)): original = layers.Input(shape=org_shape) x = Conv2D(4, (3, 3))(original) x = layers.MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3))(x) x = layers.MaxPooling2D((2, 2), padding='same')(x) ...

class DATA(): def __init__(self): num_classes = 10 ((x_train, y_train), (x_test, y_test)) = datasets.mnist.load_data() (img_rows, img_cols) = x_train.shape[1:] if (backend.image_data_format() == 'channels_first'): x_train = x_train.reshape(x_train.shape[0], 1, img_rows...

def plot_loss(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show()

class ANN(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = output(h) super().__init__(x, y) self.compile(l...

def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.boston_housing.load_data() scaler = preprocessing.MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) return ((X_train, y_train), (X_test, y_test))

def main(): Nin = 13 Nh = 5 Nout = 1 model = ANN(Nin, Nh, Nout) ((X_train, y_train), (X_test, y_test)) = Data_func() history = model.fit(X_train, y_train, epochs=100, batch_size=100, validation_split=0.2, verbose=2) performace_test = model.evaluate(X_test, y_test, batch_size=100) print...

def ANN_models_func(Nin, Nh, Nout): x = layers.Input(shape=(Nin,)) h = layers.Activation('relu')(layers.Dense(Nh)(x)) y = layers.Activation('softmax')(layers.Dense(Nout)(h)) model = models.Model(x, y) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return...

def ANN_seq_func(Nin, Nh, Nout): model = models.Sequential() model.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) model.add(layers.Dense(Nout, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model

class ANN_models_class(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') softmax = layers.Activation('softmax') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = sof...

class ANN_seq_class(models.Sequential): def __init__(self, Nin, Nh, Nout): super().__init__() self.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) self.add(layers.Dense(Nout, activation='softmax')) self.compile(loss='categorical_crossentropy', optimizer='adam', metric...

def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.mnist.load_data() Y_train = np_utils.to_categorical(y_train) Y_test = np_utils.to_categorical(y_test) (L, W, H) = X_train.shape X_train = X_train.reshape((- 1), (W * H)) X_test = X_test.reshape((- 1), (W * H)) X_train = (X_...

def plot_loss(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model Loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc=0)

def plot_acc(history): plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('Model accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc=0)

def main(): Nin = 784 Nh = 100 number_of_class = 10 Nout = number_of_class model = ANN_seq_class(Nin, Nh, Nout) ((X_train, Y_train), (X_test, Y_test)) = Data_func() history = model.fit(X_train, Y_train, epochs=15, batch_size=100, validation_split=0.2) performace_test = model.evaluate(X...

def plot_acc(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['accuracy']) plt.plot(history['val_accuracy']) if (title is not None): plt.title(title) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Training', 'Veri...

def plot_loss(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['loss']) plt.plot(history['val_loss']) if (title is not None): plt.title(title) plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Training', 'Verification'],...

class History(): def __init__(self): self.history = {'accuracy': [], 'loss': [], 'val_accuracy': [], 'val_loss': []}

class Metrics_Mean(): def __init__(self): self.reset_states() def __call__(self, loss): self.buff.append(loss.data) def reset_states(self): self.buff = [] def result(self): return np.mean(self.buff)

class Metrics_CategoricalAccuracy(): def __init__(self): self.reset_states() def __call__(self, labels, predictions): decisions = predictions.data.max(1)[1] self.correct += decisions.eq(labels.data).cpu().sum() self.L += len(labels.data) def reset_states(self): (...

class ANN_models_class(nn.Module): def __init__(self, Nin, Nh, Nout): super().__init__() self.hidden = nn.Linear(Nin, Nh) self.last = nn.Linear(Nh, Nout) self.Nin = Nin def forward(self, x): x = x.view((- 1), self.Nin) h = F.relu(self.hidden(x)) y = F....

def Data_func(): train_dataset = datasets.MNIST('~/pytorch_data', train=True, download=True, transform=transforms.ToTensor()) test_dataset = datasets.MNIST('~/pytorch_data', train=False, transform=transforms.ToTensor()) train_ds = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, s...

def ANN_models_func(Nin, Nh, Nout): x = layers.Input(shape=(Nin,)) h = layers.Activation('relu')(layers.Dense(Nh)(x)) y = layers.Activation('softmax')(layers.Dense(Nout)(h)) model = models.Model(x, y) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return...

def ANN_seq_func(Nin, Nh, Nout): model = models.Sequential() model.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) model.add(layers.Dense(Nout, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model

class ANN_models_class(models.Model): def __init__(self, Nin, Nh, Nout): hidden = layers.Dense(Nh) output = layers.Dense(Nout) relu = layers.Activation('relu') softmax = layers.Activation('softmax') x = layers.Input(shape=(Nin,)) h = relu(hidden(x)) y = sof...

class ANN_seq_class(models.Sequential): def __init__(self, Nin, Nh, Nout): super().__init__() self.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,))) self.add(layers.Dense(Nout, activation='softmax')) self.compile(loss='categorical_crossentropy', optimizer='adam', metric...

def Data_func(): ((X_train, y_train), (X_test, y_test)) = datasets.mnist.load_data() Y_train = utils.to_categorical(y_train) Y_test = utils.to_categorical(y_test) (L, W, H) = X_train.shape X_train = X_train.reshape((- 1), (W * H)) X_test = X_test.reshape((- 1), (W * H)) X_train = (X_train ...

def plot_acc(history, title=None): if (not isinstance(history, dict)): history = history.history plt.plot(history['acc']) plt.plot(history['val_acc']) if (title is not None): plt.title(title) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Training', 'Verification']...