def test_linearize():
nodes = make_caterpillar_graph(5)
linearize.linearize()
sess = create_session()
import memory_util
memory_util.vlog(1)
with memory_util.capture_stderr() as stderr:
sess.run(nodes[-1].op)
memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)
def test_linearize():
nodes = make_caterpillar_graph(5)
linearize.linearize()
sess = create_session()
import memory_util
memory_util.vlog(1)
with memory_util.capture_stderr() as stderr:
sess.run(nodes[-1].op)
memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)
def run_and_analyze(in_shape):
with memory_util.capture_stderr() as stderr:
res = sess.run(y, feed_dict={x: np.random.randn(*in_shape)})
print res.shape
expected_mem = reduce(lambda i, j: i * j, in_shape) # inputs
expected_mem += (kernel_size**2) + in_channels * out_channels # weights
expected_mem += reduce(lambda i, j: i * j, res.shape) # outputs
expected_mem *= 4 # 4 bytes per float
peak_mem = memory_util.peak_memory(stderr)
print 'expected mem usage (MB): ', expected_mem / BYTES_PER_MB
print 'peak mem usage (MB): ', peak_mem / BYTES_PER_MB
print 'peak:expected mem ratio: ', peak_mem / float(expected_mem)
print memory_util.print_memory_timeline(stderr)
memory_util.plot_memory_timeline(plt, stderr)
import ipdb
ipdb.set_trace()
def test_linearize():
nodes = make_caterpillar_graph(5)
linearize.linearize()
sess = create_session()
import memory_util
memory_util.vlog(1)
with memory_util.capture_stderr() as stderr:
sess.run(nodes[-1].op)
memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)
if __name__=='__main__':
setup_env()
import memory_util
memory_util.vlog(1)
# sess = create_session()
#nodes = make_caterpillar_graph()
# test_print()
# linearize.print_tf_graph(linearize.get_graph())
# print(tf.get_default_graph().as_graph_def())
# test_toposort()
test_linearize()
sys.exit()
# with memory_util.capture_stderr() as stderr:
# print(sess.run(nodes[-1][0,0]))
print(len(stderr.getvalue()))
memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)
config = tf.ConfigProto(
log_device_placement=False,
graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L0)))
return tf.InteractiveSession(config=config)
node_mbs = 1
length = 5
dtype = np.float32
n = node_mbs * 250000
a0_ = tf.ones((n, ), dtype=dtype)
a0 = tf.Variable(a0_, name="a0")
a = a0
for i in range(1, length):
name = "a" + str(i)
a = tf.tanh(a, name=name)
grad = tf.gradients([a], [a0])[0]
sess = create_session()
sess.run(tf.global_variables_initializer())
with memory_util.capture_stderr() as stderr:
sess.run(grad.op)
peak_memory = memory_util.peak_memory(stderr)
memory_util.print_memory_timeline(stderr)
print("Peak memory: %d" % (peak_memory, ))
PARAMS["learning_rate"] = 0.00001
PARAMS["momentum"] = 0.95
PARAMS["max_steps"] = 101
PARAMS["batch_size"] = 400 # 40 the max min-batch size it can go without memory error for training
PARAMS["test_freq"] = 2
PARAMS["tf_prec"] = "tf.float64"
PARAMS["GradScaler"] = 1.0
PARAMS["DipoleScaler"]=1.0
PARAMS["NeuronType"] = "relu"
PARAMS["HiddenLayers"] = [1000, 1000, 1000]
PARAMS["EECutoff"] = 15.0
PARAMS["EECutoffOn"] = 7.0
PARAMS["Erf_Width"] = 0.4
#PARAMS["AN1_r_Rc"] = 8.0
#PARAMS["AN1_num_r_Rs"] = 64
PARAMS["EECutoffOff"] = 15.0
PARAMS["learning_rate_dipole"] = 0.0001
PARAMS["learning_rate_energy"] = 0.00001
PARAMS["SwitchEpoch"] = 10
d = MolDigester(TreatedAtoms, name_="ANI1_Sym_Direct", OType_="EnergyAndDipole") # Initialize a digester that apply descriptor for the fragme
tset = TensorMolData_BP_Direct_EE_WithEle(a, d, order_=1, num_indis_=1, type_="mol", WithGrad_ = True)
#tset = TensorMolData_BP_Direct_EE(a, d, order_=1, num_indis_=1, type_="mol", WithGrad_ = True) # Initialize TensorMolData that contain the training data fo
#tset = TensorMolData_BP_Multipole_2_Direct(a, d, order_=1, num_indis_=1, type_="mol", WithGrad_ = False)
#manager=TFMolManage("",tset,False,"fc_sqdiff_BP_Direct_EE_ChargeEncode") # Initialzie a manager than manage the training of neural network.
#manager=TFMolManage("",tset,False,"Dipole_BP_2_Direct")
manager=TFMolManage("",tset,False,"fc_sqdiff_BP_Direct_EE_Update")
PARAMS['Profiling']=1
with memory_util.capture_stderr() as stderr:
manager.Train(1)
memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)