def not_enough_info_to_solve_test(self):
def model():
x = tf.placeholder(tf.float32, shape=(None, ))
return {'x': x, 'z': tf.norm(x)}
# Model requires 'x'
T = tfModelSession(model)
T('z')
def missing_var_in_call_test(self):
def model():
x = tf.placeholder(tf.float32, shape=(None, ))
return {'x': x, 'z': tf.norm(x)}
# There is no variable named q in the model
T = tfModelSession(model)
T('q', x=[1, 2, 3])
def l2norm_test(self):
def model():
x = tf.placeholder(tf.float32, shape=(None, ))
return {'x': x, 'z': tf.norm(x)}
T = tfModelSession(model)
x = np.linspace(0, 1, 5)
result = T('z', x=x)['z']
np.testing.assert_array_almost_equal(result, np.linalg.norm(x))
def rank_test(self):
def model():
x0 = tf.placeholder(tf.float32)
x1 = tf.placeholder(tf.float32, shape=(None, ))
x2 = tf.placeholder(tf.float32, shape=(None, 10))
return {'x0': x0, 'x1': x1, 'x2': x2}
T = tfModelSession(model)
assert_equal(T.get_info('x0')['rank'], 0)
assert_equal(T.get_info('x1')['rank'], 1)
assert_equal(T.get_info('x2')['rank'], 2)
def vector_add_test(self):
def model():
x = tf.placeholder(tf.float32, shape=(None, ))
y = tf.placeholder(tf.float32, shape=(None, ))
z = tf.add(x, y, name='z')
return {'x': x, 'y': y, 'z': z}
T = tfModelSession(model)
x = np.random.uniform(size=(10, ))
y = np.random.uniform(size=(10, ))
result = T('z', x=x, y=y)['z']
np.testing.assert_array_almost_equal(result, x + y)
def scalar_add_test(self):
def model():
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
z = tf.add(x, y)
return {'x': x, 'y': y, 'z': z}
T = tfModelSession(model)
x, y = 2, 3
result = T('z', x=x, y=y)['z']
np.testing.assert_equal(result, x + y)
def no_model_set_set_call_test(self):
T = tfModelSession()
T('z')
def no_model_set_get_var_test(self):
T = tfModelSession()
T.get_variables()
def no_vars_in_model_test(self):
def model():
pass
T = tfModelSession(model)
T.get_variables()