DoctorLoop

violin_parts = plt.violinplot(underdone['time'], positions=[0], showmeans=True)
# over-ride default blue color, you can't use 'color' parameter as can on histograms etc
for pc in violin_parts['bodies']: # used to
    pc.set_facecolor('red')
    violin_parts['cbars'].set_edgecolor('red')
    violin_parts['cmaxes'].set_edgecolor('red')
    violin_parts['cmins'].set_edgecolor('red')
    violin_parts['cmeans'].set_edgecolor('red')
    plt.violinplot(softboiled['time'], positions=[0.5], showmeans=True)
    plt.violinplot(hardboiled['time'], positions=[1], showmeans=True)

DoctorLoop

tf.keras.backend.clear_session()

model = tf.keras.Sequential([
    tf.keras.Input(shape=(2,),name="basket"),
    tf.keras.layers.Dense(16, activation="relu", name="dense_1"),
    tf.keras.layers.Dense(6, activation="relu", name="dense_2"),
    tf.keras.layers.Dense(3, activation="softmax", name="out_pred"),
])

learning_rate = 1.0e-4

DoctorLoop

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs/egg_times", histogram_freq=1)

history = model.fit(train_, 
                    target_,
                    epochs=800, 
                    verbose=0, 
                    use_multiprocessing=True, 
                    callbacks=[tensorboard_callback],
                    validation_split=0.1,
                    validation_freq=20)

DoctorLoop

class DenseModel(nn.Module):
    def __init__(self, num_in):

        super(DenseModel, self).__init__()

        self.fc1 = nn.Linear(num_in, 16)
        self.fc2 = nn.Linear(16, 6)
        self.fc3 = nn.Linear(6, 3)

    def forward(self, x):

DoctorLoop

for epoch in range(800): 

    running_loss = []
    for i, batch in enumerate(trainloader):
        
        inputs = batch["features"]
        labels = batch["outcomes"]

        optimizer.zero_grad()

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def model(data):
    fc1w_prior = dist.Normal(loc=torch.zeros_like(dense.fc1.weight), scale=torch.ones_like(dense.fc1.weight))
    fc1b_prior = dist.Normal(loc=torch.zeros_like(dense.fc1.bias), scale=torch.ones_like(dense.fc1.bias))
    
    fc2w_prior = dist.Normal(loc=torch.zeros_like(dense.fc2.weight), scale=torch.ones_like(dense.fc2.weight))
    fc2b_prior = dist.Normal(loc=torch.zeros_like(dense.fc2.bias), scale=torch.ones_like(dense.fc2.bias))
    
    fc3w_prior = dist.Normal(loc=torch.zeros_like(dense.fc3.weight), scale=torch.ones_like(dense.fc3.weight))
    fc3b_prior = dist.Normal(loc=torch.zeros_like(dense.fc3.bias), scale=torch.ones_like(dense.fc3.bias))
    

DoctorLoop

optim = pyro.optim.Adam({"lr": 0.001})
svi = SVI(model, guide, optim, loss=Trace_ELBO())

num_iterations = 50
loss = 0

for j in range(num_iterations):
    loss = 0
    for batch_id, data in enumerate(trainloader):
        # calculate the loss and take a gradient step

DoctorLoop

ax1 = plt.subplot(1,3,1)
ax1.hist(predicted[:,1].reshape(-1,1),bins=[0,1,2,3],density=False, hatch="/", color='r', edgecolor='black')
ax = plt.subplot(1,3,2, sharey=ax1)
ax.hist(predicted[:,49].reshape(-1,1),bins=[0,1,2,3],density=False, color='g', edgecolor='black')
ax = plt.subplot(1,3,3,sharey=ax1)
ax.hist(predicted[:,13].reshape(-1,1),bins=[0,1,2,3],density=False, hatch=".", edgecolor='black')

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num_samples = 5000
predicted = []
for i in range(num_samples):
    features = test_dataset.df.sample(n=3).drop('outcome',axis=1)
    batch_predictions = predict(torch.tensor(features.values))
    predicted.append(batch_predictions)

DoctorLoop

import pandas as pd
egg_timing_df = pd.read_csv("egg_times.csv",index_col=0)
egg_timing_df.head()