<jupyter_start><jupyter_text>Stochastic Allen Cahn experiments $\beta = 20$ and $\beta=10$  Symmertric models<jupyter_code>results = torch.load('../../temp/models/phi4/rnvp_phi4_N100_a0.10_b0.00e+00_beta20.00_rposinit0.10_10blocks.pyT',map_location=torch.device('cpu'))

print('Real-NVP parameters: \n', results['args_model'])

print('Target parameters: \n', results['args_target'])

model = results['model']
print('Final model loaded')

phis = model.sample(10).detach()
phis.shape
for i in range(10):
    plt.plot(np.linspace(0, 1, 100), phis[i, :])
    plt.xlabel('$s$')
    plt.ylabel('$\phi(s)$')
results = torch.load('../../temp/models/phi4/rnvp_phi4_N100_a0.10_b0.00e+00_beta10.00_rposinit0.5_10blocks.pyT', map_location=torch.device('cpu'))

print('Real-NVP parameters: \n', results['args_model'])

print('Target parameters: \n', results['args_target'])

model = results['model']
model.device = torch.device('cpu')
print('Final model loaded')

phis = model.sample(10).detach()
phis.shape
for i in range(10):
    plt.plot(np.linspace(0, 1, 100), phis[i, :])
    plt.xlabel('$s$')
    plt.ylabel('$\phi(s)$')<jupyter_output>Real-NVP parameters: 
 {'dim': 100, 'n_realnvp_block': 10, 'block_depth': 1, 'hidden_dim': 100, 'args_prior': {'type': 'coupled', 'a': 1, 'alpha': 0.1, 'beta': 10.0}, 'init_weight_scale': 1e-06, 'mask_type': 'inter'}
Target parameters: 
 {'type': 'phi4', 'N': 100, 'dim_phys': 1, 'a': 0.1, 'b': 0.0, 'beta': 10.0, 'tilt': None}
Final model loaded<jupyter_text>Tilted distribution<jupyter_code>results = torch.load('../../temp/models/phi4/rnvp_phi4_N100_a0.02_b0.00e+00_beta200.00_tv0.70_2blocks.pyT', map_location=torch.device('cpu'))

print('Real-NVP parameters: \n', results['args_model'])

print('Target parameters: \n', results['args_target'])

model = results['model']
model.device = torch.device('cpu')
print('Final model loaded')

phis = model.sample(10).detach()
phis.shape
for i in range(10):
    plt.plot(np.linspace(0, 1, 100), phis[i, :])
    plt.xlabel('$s$')
    plt.ylabel('$\phi(s)$')<jupyter_output>Real-NVP parameters: 
 {'dim': 100, 'n_realnvp_block': 2, 'block_depth': 1, 'hidden_dim': 100, 'args_prior': {'type': 'coupled', 'a': 1, 'alpha': 0.02, 'beta': 200.0}, 'init_weight_scale': 1e-06, 'mask_type': 'half'}
Target parameters: 
 {'type': 'phi4', 'N': 100, 'dim_phys': 1, 'a': 0.02, 'b': 0.0, 'beta': 200.0, 'tilt': {'val': 0.7, 'lambda': 10000.0}}
Final model loaded<jupyter_text>Stochastic Allen-Cahn with uniformed "white" base disctribution<jupyter_code>results = torch.load('../../temp/models/phi4/rnvp_phi4_N100_a0.10_b0.00e+00_beta20.00_rposinit0.10_10blocks_uninformed.pyT', map_location=torch.device('cpu'))

print('Real-NVP parameters: \n', results['args_model'])

print('Target parameters: \n', results['args_target'])

model = results['model']
model.device = torch.device('cpu')
print('Final model loaded')

phis = model.sample(10).detach()
phis.shape
for i in range(10):
    plt.plot(np.linspace(0, 1, 100), phis[i, :])
    plt.xlabel('$s$')
    plt.ylabel('$\phi(s)$')<jupyter_output>Real-NVP parameters: 
 {'dim': 100, 'n_realnvp_block': 10, 'block_depth': 1, 'hidden_dim': 10, 'args_prior': {'type': 'uncoupled', 'a': 0.1, 'alpha': 0.1, 'beta': 20.0}, 'init_weight_scale': 1e-06, 'mask_type': 'half'}
Target parameters: 
 {'type': 'phi4', 'N': 100, 'dim_phys': 1, 'a': 0.1, 'b': 0.0, 'beta': 20.0}
Final model loaded
Final model loaded
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