block_sigmoid_normal.py

from deepreplay.callbacks import ReplayData
from deepreplay.replay import Replay
from deepreplay.plot import compose_plots
from keras.initializers import normal
from matplotlib import pyplot as plt

filename = 'part2_weight_initializers.h5'
group_name = 'sigmoid_stdev_0.01'

# Uses normal initializer
initializer = normal(mean=0, stddev=0.01, seed=13)

# Builds BLOCK model
model = build_model(n_layers=5, input_dim=10, units=100, 
                    activation='sigmoid', initializer=initializer)

# Since we only need initial weights, we don't even need to train the model! 
# We still use the ReplayData callback, but we can pass the model as argument instead
replaydata = ReplayData(X, y, filename=filename, group_name=group_name, model=model)

# Now we feed the data to the actual Replay object
# so we can build the visualizations
replay = Replay(replay_filename=filename, group_name=group_name)

# Using subplot2grid to assemble a complex figure...
fig = plt.figure(figsize=(12, 6))
ax_zvalues = plt.subplot2grid((2, 2), (0, 0))
ax_weights = plt.subplot2grid((2, 2), (0, 1))
ax_activations = plt.subplot2grid((2, 2), (1, 0))
ax_gradients = plt.subplot2grid((2, 2), (1, 1))

wv = replay.build_weights(ax_weights)
gv = replay.build_gradients(ax_gradients)
# Z-values
zv = replay.build_outputs(ax_zvalues, before_activation=True, 
                          exclude_outputs=True, include_inputs=False)
# Activations
av = replay.build_outputs(ax_activations, exclude_outputs=True, include_inputs=False)

# Finally, we use compose_plots to update all
# visualizations at once
fig = compose_plots([zv, wv, av, gv], 
                    epoch=0, 
                    title=r'Activation: sigmoid - Initializer: Normal $\sigma = 0.01$')