import tensorflow as tf
import numpy as np
from libs.utils import weight_variable, bias_variable, montage_batch


# %%
def VAE(input_shape=[None, 784],
        n_components_encoder=2048,
        n_components_decoder=2048,
        n_hidden=2,
        debug=False):
    # %%
    # Input placeholder
...

import tensorflow as tf
import numpy as np
import math
from libs.activations import lrelu
from libs.utils import corrupt


# %%
def autoencoder(input_shape=[None, 784],
                n_filters=[1, 10, 10, 10],
                filter_sizes=[3, 3, 3, 3],
                corruption=False):
    &qu...

# %%
import tensorflow as tf
from libs.connections import conv2d, linear
from collections import namedtuple
from math import sqrt


# %%
def residual_network(x, n_outputs,
                     activation=tf.nn.relu):
    """Builds a residual network.

    Parameters
    ----------
...

import tensorflow as tf
import numpy as np
import math
from libs.utils import corrupt


# %%
def autoencoder(dimensions=[784, 512, 256, 64]):
    """Build a deep denoising autoencoder w/ tied weights.

    Parameters
    ----------
    dimensions : list, optional
        The number...

# %% Imports
import tensorflow as tf
import numpy as np
import math


# %% Autoencoder definition
def autoencoder(dimensions=[784, 512, 256, 64]):
    """Build a deep autoencoder w/ tied weights.

    Parameters
    ----------
    dimensions : list, optional
        The number of n...

# %% Imports
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
from libs.utils import *
import matplotlib.pyplot as plt


# %% Setup input to the network and true output label.  These are
# simply placeholders which we'll fill in later.
mnist = input_...

# %%
import tensorflow as tf
from libs.batch_norm import batch_norm
from libs.activations import lrelu
from libs.connections import conv2d, linear
from libs.datasets import MNIST


# %% Setup input to the network and true output label.  These are
# simply placeholders which we'll fill in late...

# pip3 install --upgrade
# https://storage.googleapis.com/tensorflow/mac/tensorflow-0.6.0-py3-none-any.whl
# %%
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import numpy as np
import matplotlib.pyplot as plt


# %%
# get the classic mnist dataset
# o...

# %% imports
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt


# %% Let's create some toy data
plt.ion()
n_observations = 100
fig, ax = plt.subplots(1, 1)
xs = np.linspace(-3, 3, n_observations)
ys = np.sin(xs) + np.random.uniform(-0.5, 0.5, n_observations)
ax.scatt...

# %% Imports
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt


# %% Let's create some toy data
plt.ion()
n_observations = 100
fig, ax = plt.subplots(1, 1)
xs = np.linspace(-3, 3, n_observations)
ys = np.sin(xs) + np.random.uniform(-0.5, 0.5, n_observations)
ax.scatt...