def test_tf_load(self):
linear = Linear(10, 2)()
sigmoid = Sigmoid()(linear)
softmax = SoftMax().set_name("output")(sigmoid)
model = BModel(linear, softmax)
input = np.random.random((4, 10))
tmp_path = create_tmp_path() + "/model.pb"
model.save_tensorflow([("input", [4, 10])], tmp_path)
model_reloaded = Net.load_tf(tmp_path, ["input"], ["output"])
expected_output = model.forward(input)
output = model_reloaded.forward(input)
self.assert_allclose(output, expected_output)
def main():
tf.set_random_seed(1234)
input = tf.placeholder(tf.float32, [None, 5])
weight = tf.Variable(tf.random_uniform([5, 10]))
bias = tf.Variable(tf.random_uniform([10]))
middle = tf.nn.bias_add(tf.matmul(input, weight), bias)
output = tf.nn.tanh(middle)
tensor = np.random.rand(5, 5)
# construct BigDL model and get the result form
bigdl_model = Model(input, output, model_type="tensorflow")
bigdl_result = bigdl_model.forward(tensor)
# get result from tensorflow and compare
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
tensorflow_result = sess.run(output, {input: tensor})
print("Tensorflow forward result is " + str(tensorflow_result))
print("BigDL forward result is " + str(bigdl_result))
np.testing.assert_almost_equal(tensorflow_result, bigdl_result, 6)
print("The results are almost equal in 6 decimals")