X = T.fmatrix('X')
epsilon = T.fmatrix()
# Make random crop from X
crop_mask, updates = theano.scan(fn=random_crop, sequences=[X])
crop_X = X * crop_mask
# Creating the masks
reconstruction_dims, updates = theano.scan(fn=MASK_blanking, sequences=[crop_X])
prediction_dims = crop_mask - reconstruction_dims
# model(input_data, epsilon, w_h, mu_w, sig_w, w_h2, w_o, b_h, mu_b, sig_b, b_h2, b_o):
# Initialize Weights
weights_layer_1 = init_weights((n_visible_units, n_hidden_units), name='weights_layer_1')
bias_layer_1 = init_biases(n_hidden_units, name='bias_layer_1')
weights_layer_mu = init_weights((n_hidden_units, n_z), name='weights_layer_mu')
bias_layer_mu = init_biases(n_z, name='bias_layer_mu')
weights_layer_sig = init_weights((n_hidden_units, n_z), name='weights_layer_sig')
bias_layer_sig = init_biases(n_z, name='bias_layer_sig')
weights_layer_2 = init_weights((n_z, n_hidden_units), name='weights_layer_2')
bias_layer_2 = init_biases(n_hidden_units, name='bias_layer_2')
weights_layer_y = init_weights((n_hidden_units, n_visible_units), name='weights_layer_y')
bias_layer_y = init_biases(n_visible_units, name='bias_layer_y')
# Build Model
model_input = crop_X * reconstruction_dims
X_hat, mu, sig = model(model_input, epsilon, weights_layer_1, weights_layer_mu, weights_layer_sig, weights_layer_2,
weights_layer_y, bias_layer_1, bias_layer_mu, bias_layer_sig, bias_layer_2, bias_layer_y)
# Define parameters
learning_rate = 0.1
n_hidden_units = 100
n_visible_units = 784
blank_proportion = .1
alpha = .5
batch_size = 100
epochs = 10
X = T.fmatrix('X')
# Initialize Weights
weights_layer_1 = init_weights((n_visible_units, n_hidden_units),
name='weights_layer_1')
bias_layer_1 = init_biases(n_hidden_units, name='bias_layer_1')
weights_layer_y = init_weights((n_hidden_units, n_visible_units),
name='weights_layer_y')
bias_layer_y = init_biases(n_visible_units, name='bias_layer_y')
# Build Model
X_hat = model(X, weights_layer_1, weights_layer_y, bias_layer_1, bias_layer_y)
# Loss
L = -T.sum(X * T.log(X_hat) + (1 - X) * T.log(1 - X_hat), axis=1)
loss = T.mean(L)
# Parameter Updating
params = [weights_layer_y, bias_layer_y, weights_layer_1, bias_layer_1]
updates = sgd(loss, params, lr=learning_rate)
return output
# Define parameters
learning_rate = 0.1
n_hidden_units = 100
n_visible_units = 784
blank_proportion = .1
alpha = .5
batch_size = 100
epochs = 10
X = T.fmatrix('X')
# Initialize Weights
weights_layer_1 = init_weights((n_visible_units, n_hidden_units), name='weights_layer_1')
bias_layer_1 = init_biases(n_hidden_units, name='bias_layer_1')
weights_layer_y = init_weights((n_hidden_units, n_visible_units), name='weights_layer_y')
bias_layer_y = init_biases(n_visible_units, name='bias_layer_y')
# Build Model
X_hat = model(X, weights_layer_1, weights_layer_y, bias_layer_1, bias_layer_y)
# Loss
L = - T.sum(X * T.log(X_hat) + (1 - X) * T.log(1 - X_hat), axis=1)
loss = T.mean(L)
# Parameter Updating
params = [weights_layer_y, bias_layer_y, weights_layer_1, bias_layer_1]
updates = sgd(loss, params, lr=learning_rate)
# Compiling
x = x.reshape(-1, 1, 7, 16)
f(x).shape
jets = T.fmatrix('Jets')
lepton_missing_E = T.fmatrix('lepton_and_missing_energy')
high_level = T.fmatrix('high_level')
n_input_units = 28
n_hidden_units = 300
n_output_units = 1
# Initialize Weights
w_h = init_weights((n_input_units, n_hidden_units),
name='weights_layer_1',
scale=0.1)
b_h = init_biases(n_hidden_units, name='bias_layer_1')
w_h2 = init_weights((n_hidden_units, n_hidden_units), name='weights_layer_2')
b_h2 = init_biases(n_hidden_units, name='bias_layer_2')
w_h3 = init_weights((n_hidden_units, n_hidden_units), name='weights_layer_3')
b_h3 = init_biases(n_hidden_units, name='bias_layer_3')
w_h4 = init_weights((n_hidden_units, n_hidden_units), name='weights_layer_4')
b_h4 = init_biases(n_hidden_units, name='bias_layer_4')
w_o = init_weights((n_hidden_units, n_output_units),
name='weights_layer_y',
scale=0.001)
b_o = init_biases(n_output_units, name='bias_layer_y')
noise_h, noise_h2, noise_h3, noise_h4, noise_py_x = model(
X, w_h, w_h2, w_h3, w_h4, w_o, b_h, b_h2, b_h3, b_h4, b_o, 0.8,
[0.5, 0.5, 0.5, 0.5])