def main():
# Generate and split data
# Try and play with arguments
all_data = data.generate_data_gauss(numSamples=1000, noise=0.5)
train_data, valid_data = data.split_data(all_data, val_factor=0.3)
# Set show to True if you want to see generated dataset
data.plot_data(train_data, valid_data, show=False)
# Directory to save summaries to
# From your conda environment run
# tensorbard --logdir ../tf_playground/output
# to see training details
output = utils.get_output_dir()
# Create model
# Go to model.py file to make changes to the model
model = Model()
# Lets train
# Try changing number of epochs and batch_size
trainer = Trainer(train_data=train_data,
valid_data=valid_data,
model=model,
epochs=10,
batch_size=2,
output=output)
trainer.train()
trainer.save_final_accuracy()
def simpleLinearRegression(data):
## Linear Regression
plot_data(x=data['sqft_living'],
y=data['price'],
filename='price_sqft.png')
linear_regression(data=data,
x='sqft_living',
y='price',
filename='linear_regression.png')
def _save_val_predictions_plot(self):
fig = plot_data(self.val_predictions)
img = utils.plot_to_image(fig)
summary = tf.Summary(value=[
tf.Summary.Value(tag="Val predictions",
image=tf.Summary.Image(
encoded_image_string=img, height=6, width=6))
])
self.valid_summary_writer.add_summary(summary, self._epochs_training)
def read_excel_data():
global measurements_dict, selected_option
files = []
while True:
_files = [f for f in os.listdir('measurements') if f[-4:] == 'xlsx']
if _files != files:
files = _files
measurements_dict = {}
for f in files:
df = read_data('measurements/%s' % f)
X = df.to_numpy()
fig = plot_data(X)
tab, df_tab = table_html(X)
measurements_dict[f] = [tab, df_tab, fig]
if selected_option == None:
selected_option = f
time.sleep(1)
def simpleLinearRegression(data):
## Linear Regression
plot_data(x=data['sqft_living'], y=data['price'], filename='price_sqft.png')
linear_regression(data=data, x='sqft_living', y='price', filename='linear_regression.png')
from model import Perceptron, LinearRegression # MAIN SCRIPT: # --- Classification --- # Generates some linearly separable data and applies the perceptron # Results are plotted after instantiation, one instance, one epoch, and completion # xs, ys = generate_data(binary=True) # myPerceptron = Perceptron() # plot_data(xs, ys, myPerceptron) # myPerceptron.train(xs[0], ys[0]) # plot_data(xs, ys, myPerceptron) # myPerceptron.fit(xs, ys, max_epochs=1) # plot_data(xs, ys, myPerceptron) # myPerceptron.fit(xs, ys) # plot_data(xs, ys, myPerceptron, final=True) # # --- Linear Regression --- # # Generates some linear data and applies linear regression # # Results are plotted after instantiation, one instance, one epoch, and convergence xs, ys = generate_data(binary=False) myLinearRegression = LinearRegression() plot_data(xs, ys, myLinearRegression) myLinearRegression.train(xs[0], ys[0]) plot_data(xs, ys, myLinearRegression) myLinearRegression.fit(xs, ys, max_epochs=1) plot_data(xs, ys, myLinearRegression) myLinearRegression.fit(xs, ys) plot_data(xs, ys, myLinearRegression, final=True) print(myLinearRegression)
def test(self, ld_te, loss_fn=None, iw_fn=None, ld_name='', verbose=False):
"""This function compute precision and coverage of the psuedo-labeling function."""
# compute precision
prec_vec = []
n_conf = 0
n = 0
for x, y in ld_te:
prec_i, n_conf_i = self.loss_fn_test(
x,
y,
lambda x: self.model_pred(x, training=False)['logits'],
self.model,
model_iw=iw_fn,
reduce='none')
prec_vec.append(prec_i)
n_conf += n_conf_i
n += y.shape[0]
prec = tf.math.reduce_mean(tf.cast(tf.concat(prec_vec, 0), tf.float32))
if verbose:
## print
print(
'[test%s] T = %f, precision = %.2f%%, size = %d/%d = %.2f%%' %
(ld_name if ld_name is '' else ' on %s' %
(ld_name), self.model.T if hasattr(self.model, 'T') else -1,
prec * 100.0, n_conf, n, float(n_conf) / float(n) * 100.0))
## visualize for 2d data
x_list = []
y_list = []
show = True
for x, y in ld_te:
if x.shape[-1] is not 2 or any(y > 1):
show = False
break
conf = self.model(x)
i_conf = conf == 1
x_list.append(x)
y_list.append(y + 2 * tf.cast(i_conf, tf.int64))
if show:
x_list = tf.concat(x_list, 0).numpy()
y_list = tf.concat(y_list, 0).numpy()
plot_data(
x_list,
y_list,
markers=['s', 's', 's', 's'],
colors=['r', 'g', 'k', 'k'],
facecolors=['r', 'g', 'r', 'g'],
alphas=[0.5, 0.5, 1.0, 1.0],
labels=[
r'$-$', r'$+$', r'$-$' + ' (conf)', r'$+$' + ' (conf)'
],
markersize=4,
linewidth=2,
classifier=lambda x: tf.nn.softmax(
self.model_pred(tf.constant(x, dtype=tf.float32),
training=False)['logits'], -1).numpy(),
fn=os.path.join(self.params.save_root,
"conf_examples_%s" % (ld_name)),
)
return prec, n_conf, n