def test_1(self):
# single passthrough
(train_data_node,
train_labels_node,
validation_data_node,
test_data_node,
# predictions
train_prediction,
validation_prediction,
test_prediction,
# weights
conv1_weights,
conv2_weights,
fc1_weights,
fc2_weights,
# training
optimizer,
loss,
learning_rate,
summaries) = mnist.build_model()
with tf.Session() as s:
tf.initialize_all_variables().run()
print('Variables initialized')
step=0
with np.load(data_file) as data:
feed_dict = {
train_data_node: data['batch_data'],
train_labels_node: data['batch_labels']
}
evals=[train_prediction]
status,result=tdb.debug(evals, feed_dict=feed_dict, breakpoints=None, break_immediately=False, session=s)
self.assertEqual(status,tdb.FINISHED)
def test_1(self):
# single passthrough
(train_data_node,
train_labels_node,
validation_data_node,
test_data_node,
# predictions
train_prediction,
validation_prediction,
test_prediction,
# weights
conv1_weights,
conv2_weights,
fc1_weights,
fc2_weights,
# training
optimizer,
loss,
learning_rate,
summaries) = mnist.build_model()
with tf.Session() as s:
tf.initialize_all_variables().run()
print('Variables initialized')
step=0
with np.load("mnist_0.npz") as data:
feed_dict = {
train_data_node: data['batch_data'],
train_labels_node: data['batch_labels']
}
evals=[train_prediction]
status,result=tdb.debug(evals, feed_dict=feed_dict, breakpoints=None, break_immediately=False, session=s)
self.assertEqual(status,tdb.FINISHED)
def test_2(self):
"""
mnist with plotting
"""
(train_data_node,
train_labels_node,
validation_data_node,
test_data_node,
# predictions
train_prediction,
validation_prediction,
test_prediction,
# weights
conv1_weights,
conv2_weights,
fc1_weights,
fc2_weights,
# training
optimizer,
loss,
learning_rate,
summaries) = mnist.build_model()
s=tf.InteractiveSession()
tf.initialize_all_variables().run()
# use the same input every time for this test
with np.load(data_file) as data:
a=data['batch_data']
b=data['batch_labels']
feed_dict = {
train_data_node: a,
train_labels_node: b
}
# pdb.set_trace()
# result=s.run(optimizer,feed_dict)
# pdb.set_trace()
# tmp
# return
evals=[optimizer,loss,train_prediction,conv1_weights,conv2_weights,fc1_weights,fc2_weights]
# define some plotting functions
# use one debugSession per run
# attach plot nodes
g=tf.get_default_graph()
p1=tdb.plot_op(viz.viz_conv_weights,inputs=[g.as_graph_element(conv1_weights)])
p2=tdb.plot_op(viz.viz_conv_weights,inputs=[g.as_graph_element(conv2_weights)])
p3=tdb.plot_op(viz.viz_fc_weights,inputs=[g.as_graph_element(fc1_weights)])
p4=tdb.plot_op(viz.viz_fc_weights,inputs=[g.as_graph_element(fc2_weights)])
# get the plot op by name and
evals=[optimizer, loss, learning_rate, train_prediction, p1,p2,p3,p4]
status,result=tdb.debug(evals, feed_dict=feed_dict, session=s)
def test_2(self):
"""
mnist with plotting
"""
(train_data_node,
train_labels_node,
validation_data_node,
test_data_node,
# predictions
train_prediction,
validation_prediction,
test_prediction,
# weights
conv1_weights,
conv2_weights,
fc1_weights,
fc2_weights,
# training
optimizer,
loss,
learning_rate,
summaries) = mnist.build_model()
s=tf.InteractiveSession()
tf.initialize_all_variables().run()
# use the same input every time for this test
with np.load("mnist_0.npz") as data:
a=data['batch_data']
b=data['batch_labels']
feed_dict = {
train_data_node: a,
train_labels_node: b
}
# pdb.set_trace()
# result=s.run(optimizer,feed_dict)
# pdb.set_trace()
# tmp
# return
evals=[optimizer,loss,train_prediction,conv1_weights,conv2_weights,fc1_weights,fc2_weights]
# define some plotting functions
# use one debugSession per run
# attach plot nodes
g=tf.get_default_graph()
p1=tdb.plot_op(viz.viz_conv_weights,inputs=[g.as_graph_element(conv1_weights)])
p2=tdb.plot_op(viz.viz_conv_weights,inputs=[g.as_graph_element(conv2_weights)])
p3=tdb.plot_op(viz.viz_fc_weights,inputs=[g.as_graph_element(fc1_weights)])
p4=tdb.plot_op(viz.viz_fc_weights,inputs=[g.as_graph_element(fc2_weights)])
# get the plot op by name and
evals=[optimizer, loss, learning_rate, train_prediction, p1,p2,p3,p4]
status,result=tdb.debug(evals, feed_dict=feed_dict, session=s)
validation_data_node,
test_data_node,
# predictions
train_prediction,
validation_prediction,
test_prediction,
# weights
conv1_weights,
conv2_weights,
fc1_weights,
fc2_weights,
# training
optimizer,
loss,
learning_rate,
summaries) = mnist.build_model()
def viz_activations(ctx, m):
plt.matshow(m.T, cmap=plt.cm.gray)
plt.title("LeNet Predictions")
plt.xlabel("Batch")
plt.ylabel("Digit Activation")
# plotting a user-defined function 'viz_activations'
p0 = tdb.plot_op(viz_activations, inputs=[train_prediction])
# weight variables are of type tf.Variable, so we need to find the corresponding tf.Tensor instead
g = tf.get_default_graph()
p1 = tdb.plot_op(viz.viz_conv_weights,
inputs=[g.as_graph_element(conv1_weights)])
