def __init__(self, n_tasks, n_features, logdir=None, layer_sizes=[1000], weight_init_stddevs=[.02], bias_init_consts=[1.], penalty=0.0, penalty_type="l2", dropouts=[0.5], learning_rate=0.002, momentum=.8, optimizer="adam", batch_size=50, fit_transformers=[], n_evals=1, verbose=True, seed=None, **kwargs): """Initialize TensorflowMultiTaskFitTransformRegressor Parameters ---------- n_tasks: int Number of tasks n_features: list or int Number of features. logdir: str Location to save data layer_sizes: list List of layer sizes. weight_init_stddevs: list List of standard deviations for weights (sampled from zero-mean gaussians). One for each layer. bias_init_consts: list List of bias initializations. One for each layer. penalty: float Amount of penalty (l2 or l1 applied) penalty_type: str Either "l2" or "l1" dropouts: list List of dropout amounts. One for each layer. learning_rate: float Learning rate for model. momentum: float Momentum. Only applied if optimizer=="momentum" optimizer: str Type of optimizer applied. batch_size: int Size of minibatches for training. fit_transformers: list List of dc.trans.FitTransformer objects n_evals: int Number of evalations per example at predict time verbose: True Perform logging. seed: int If not none, is used as random seed for tensorflow. """ self.fit_transformers = fit_transformers self.n_evals = n_evals # Run fit transformers on dummy dataset to determine n_features after transformation if isinstance(n_features, list): X_b = np.ones([batch_size] + n_features) elif isinstance(n_features, int): X_b = np.ones([batch_size, n_features]) else: raise ValueError("n_features should be list or int") for transformer in self.fit_transformers: X_b = transformer.X_transform(X_b) n_features = X_b.shape[1] print("n_features after fit_transform: %d" % int(n_features)) TensorflowGraphModel.__init__(self, n_tasks, n_features, logdir=logdir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=bias_init_consts, penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=learning_rate, momentum=momentum, optimizer=optimizer, batch_size=batch_size, pad_batches=False, verbose=verbose, seed=seed, **kwargs)
def __init__(self, n_tasks, K=10, logdir=None, n_classes=2, penalty=0.0, penalty_type="l2", learning_rate=0.001, momentum=.8, optimizer="adam", batch_size=50, verbose=True, seed=None, **kwargs): """Initialize TensorflowMultiTaskIRVClassifier Parameters ---------- n_tasks: int Number of tasks K: int Number of nearest neighbours used in classification logdir: str Location to save data n_classes: int number of different labels penalty: float Amount of penalty (l2 or l1 applied) penalty_type: str Either "l2" or "l1" learning_rate: float Learning rate for model. momentum: float Momentum. Only applied if optimizer=="momentum" optimizer: str Type of optimizer applied. batch_size: int Size of minibatches for training. verbose: True Perform logging. seed: int If not none, is used as random seed for tensorflow. """ self.n_tasks = n_tasks self.K = K self.n_features = 2 * self.K * self.n_tasks print("n_features after fit_transform: %d" % int(self.n_features)) TensorflowGraphModel.__init__(self, n_tasks, self.n_features, logdir=logdir, layer_sizes=None, weight_init_stddevs=None, bias_init_consts=None, penalty=penalty, penalty_type=penalty_type, dropouts=None, n_classes=n_classes, learning_rate=learning_rate, momentum=momentum, optimizer=optimizer, batch_size=batch_size, pad_batches=False, verbose=verbose, seed=seed, **kwargs)
def __init__(self, n_tasks, n_features, logdir=None, layer_sizes=[1000], weight_init_stddevs=[.02], bias_init_consts=[1.], penalty=0.0, penalty_type="l2", dropouts=[0.5], learning_rate=0.002, momentum=.8, optimizer="adam", batch_size=50, fit_transformers=[], n_evals=1, verbose=True, seed=None, **kwargs): """Initialize TensorflowMultiTaskFitTransformRegressor Parameters ---------- n_tasks: int Number of tasks n_features: list or int Number of features. logdir: str Location to save data layer_sizes: list List of layer sizes. weight_init_stddevs: list List of standard deviations for weights (sampled from zero-mean gaussians). One for each layer. bias_init_consts: list List of bias initializations. One for each layer. penalty: float Amount of penalty (l2 or l1 applied) penalty_type: str Either "l2" or "l1" dropouts: list List of dropout amounts. One for each layer. learning_rate: float Learning rate for model. momentum: float Momentum. Only applied if optimizer=="momentum" optimizer: str Type of optimizer applied. batch_size: int Size of minibatches for training. fit_transformers: list List of dc.trans.FitTransformer objects n_evals: int Number of evalations per example at predict time verbose: True Perform logging. seed: int If not none, is used as random seed for tensorflow. """ self.fit_transformers = fit_transformers self.n_evals = n_evals # Run fit transformers on dummy dataset to determine n_features after transformation if isinstance(n_features, list): X_b = np.ones([batch_size] + n_features) elif isinstance(n_features, int): X_b = np.ones([batch_size, n_features]) else: raise ValueError("n_features should be list or int") for transformer in self.fit_transformers: X_b = transformer.X_transform(X_b) n_features = X_b.shape[1] print("n_features after fit_transform: %d" % int(n_features)) TensorflowGraphModel.__init__( self, n_tasks, n_features, logdir=logdir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=bias_init_consts, penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=learning_rate, momentum=momentum, optimizer=optimizer, batch_size=batch_size, pad_batches=False, verbose=verbose, seed=seed, **kwargs)
def __init__(self, n_tasks, K=10, logdir=None, n_classes=2, penalty=0.0, penalty_type="l2", learning_rate=0.001, momentum=.8, optimizer="adam", batch_size=50, verbose=True, seed=None, **kwargs): """Initialize TensorflowMultiTaskIRVClassifier Parameters ---------- n_tasks: int Number of tasks K: int Number of nearest neighbours used in classification logdir: str Location to save data n_classes: int number of different labels penalty: float Amount of penalty (l2 or l1 applied) penalty_type: str Either "l2" or "l1" learning_rate: float Learning rate for model. momentum: float Momentum. Only applied if optimizer=="momentum" optimizer: str Type of optimizer applied. batch_size: int Size of minibatches for training. verbose: True Perform logging. seed: int If not none, is used as random seed for tensorflow. """ self.n_tasks = n_tasks self.K = K self.n_features = 2 * self.K * self.n_tasks print("n_features after fit_transform: %d" % int(self.n_features)) TensorflowGraphModel.__init__( self, n_tasks, self.n_features, logdir=logdir, layer_sizes=None, weight_init_stddevs=None, bias_init_consts=None, penalty=penalty, penalty_type=penalty_type, dropouts=None, n_classes=n_classes, learning_rate=learning_rate, momentum=momentum, optimizer=optimizer, batch_size=batch_size, pad_batches=False, verbose=verbose, seed=seed, **kwargs)