#Params object for mono_dram

class DramParams(object):
    ##Bookkeeping params
    #Base output directory
    out_dir            = "/home/slundquist/mountData/ram/"
    #Inner run directory
    run_dir            = out_dir + "/mono_dram/"

    #Save parameters
    save_period        = 100000
    #output plots directory
    plot_period        = 10000
    eval_period        = 1718 # 1 epoch
    #Progress step
    progress           = 100
    #Controls how often to write out to tensorboard
    write_step         = 300
    #Flag for loading weights from checkpoint
    load               = False
    load_file          = ""
    #Device to run on
    device             = None #device

    #data params
    num_train_examples = None #dataObj.num_train_examples

    #RAM params
    win_size           = 12       #The size of each glimpse in pixels in both x and y dimension
    batch_size         = 128      #Batch size of training
    eval_batch_size    = 100      #Batch size of testing
    loc_std            = 0.03    #Standard deviation of random noise added to locations
    det_eval           = False   #If true, use only mean of location network for evaluation
    original_size      = None    #dataObj.inputShape #Size of the input image in (y, x, f)
    glimpse_scales     = 2       #Number of channels in input image
    sensor_size        = win_size**2 * glimpse_scales #Total size of input glimpse
    hg_size            = 256     #Number of features in hidden layer for glimpse encode
    #hl_size            = 256     #Number of features in hidden layer for location encode
    g_size             = 256     #Number of features in second layer (combine g and l)
    l_size             = 256     #Number of features in hidden layer for location network
    loc_dim            = 2       #Number of dimensions used in the location output
    cell_size_0        = 512     #Size of hidden latent space of first LSTM layer
    cell_size_1        = 512     #Size of hidden latent space of second LSTM layer
    classnet_size      = 256
    num_glimpses       = 4       #Number of total glimpses
    num_classes        = 10      #Number of output classes
    max_grad_norm      = 5.      #Clipping norm for gradient clipping
    reinforce_lambda   = 1

    loc_pixel_ratio    = .15     #Ratio of coord unit to image width unit

    num_steps          = 500001  #Number of total steps
    lr_start           = 1e-3    #Starting learning rate for lr decay
    lr_min             = 0       #Minimum learning rate for lr decay
    lr_decay           = .97     #Learning rate decay multiplier

    # Monte Carlo sampling
    M                  = 10

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class DramParams(object):
    out_dir = '/home/slundquist/mountData/ram/'
    run_dir = out_dir + '/mono_dram/'
    save_period = 100000
    plot_period = 10000
    eval_period = 1718
    progress = 100
    write_step = 300
    load = False
    load_file = ''
    device = None
    num_train_examples = None
    win_size = 12
    batch_size = 128
    eval_batch_size = 100
    loc_std = 0.03
    det_eval = False
    original_size = None
    glimpse_scales = 2
    sensor_size = win_size ** 2 * glimpse_scales
    hg_size = 256
    g_size = 256
    l_size = 256
    loc_dim = 2
    cell_size_0 = 512
    cell_size_1 = 512
    classnet_size = 256
    num_glimpses = 4
    num_classes = 10
    max_grad_norm = 5.0
    reinforce_lambda = 1
    loc_pixel_ratio = 0.15
    num_steps = 500001
    lr_start = 0.001
    lr_min = 0
    lr_decay = 0.97
    M = 10

#Params object for mono_dram

class DramParams(object):
    ##Bookkeeping params
    #Base output directory
    out_dir            = "/home/slundquist/mountData/ram/"
    #Inner run directory
    run_dir            = out_dir + "/mono_dram/"

    #Save parameters
    save_period        = 100000
    #output plots directory
    plot_period        = 10000
    eval_period        = 1718 # 1 epoch
    #Progress step
    progress           = 100
    #Controls how often to write out to tensorboard
    write_step         = 300
    #Flag for loading weights from checkpoint
    load               = False
    load_file          = ""
    #Device to run on
    device             = None #device

    #data params
    num_train_examples = None #dataObj.num_train_examples

    #RAM params
    win_size           = 12       #The size of each glimpse in pixels in both x and y dimension
    batch_size         = 128      #Batch size of training
    eval_batch_size    = 100      #Batch size of testing
    loc_std            = 0.03    #Standard deviation of random noise added to locations
    det_eval           = False   #If true, use only mean of location network for evaluation
    original_size      = None    #dataObj.inputShape #Size of the input image in (y, x, f)
    glimpse_scales     = 2       #Number of channels in input image
    sensor_size        = win_size**2 * glimpse_scales #Total size of input glimpse
    hg_size            = 256     #Number of features in hidden layer for glimpse encode
    #hl_size            = 256     #Number of features in hidden layer for location encode
    g_size             = 256     #Number of features in second layer (combine g and l)
    l_size             = 256     #Number of features in hidden layer for location network
    loc_dim            = 2       #Number of dimensions used in the location output
    cell_size_0        = 512     #Size of hidden latent space of first LSTM layer
    cell_size_1        = 512     #Size of hidden latent space of second LSTM layer
    classnet_size      = 256
    num_glimpses       = 4       #Number of total glimpses
    num_classes        = 10      #Number of output classes
    max_grad_norm      = 5.      #Clipping norm for gradient clipping
    reinforce_lambda   = 1

    loc_pixel_ratio    = .15     #Ratio of coord unit to image width unit

    num_steps          = 500001  #Number of total steps
    lr_start           = 1e-3    #Starting learning rate for lr decay
    lr_min             = 0       #Minimum learning rate for lr decay
    lr_decay           = .97     #Learning rate decay multiplier

    # Monte Carlo sampling
    M                  = 10
