def test_graph_save(self):
n_samples = 10
n_features = 11
n_tasks = 1
batch_size = 10
X = np.random.rand(batch_size, n_samples, n_features)
y = np.ones(shape=(n_samples, n_tasks))
ids = np.arange(n_samples)
dataset = dc.data.NumpyDataset(X, y, None, ids)
g = TensorGraph(model_dir='/tmp/tmpss5_ki5_')
inLayer = Input(shape=(None, n_samples, n_features))
g.add_feature(inLayer)
flatten = Flatten()
g.add_layer(flatten, parents=[inLayer])
dense = Dense(out_channels=1)
g.add_layer(dense, parents=[flatten])
g.add_output(dense)
label_out = Input(shape=(None, 1))
g.add_label(label_out)
loss = LossLayer()
g.add_layer(loss, parents=[dense, label_out])
g.set_loss(loss)
g.fit(dataset, nb_epoch=100)
g.save()
g1 = TensorGraph.load_from_dir('/tmp/tmpss5_ki5_')
print(g1)
print(g1.predict_on_batch(X))
def test_get_layer_variable_values(self):
"""Test to get the variable values associated with a layer"""
# Test for correct value return (normal mode)
tg = dc.models.TensorGraph()
var = Variable([10.0, 12.0])
tg.add_output(var)
expected = [10.0, 12.0]
obtained = tg.get_layer_variable_values(var)[0]
np.testing.assert_array_equal(expected, obtained)
# Test for shapes (normal mode)
tg = dc.models.TensorGraph()
input_tensor = Input(shape=(10, 100))
output = Dense(out_channels=20, in_layers=[input_tensor])
tg.add_output(output)
expected_shape = (100, 20)
obtained_shape = tg.get_layer_variable_values(output)[0].shape
assert expected_shape == obtained_shape
def test_input(self):
"""Test that Input can be invoked."""
in_shape = (4, 3)
with self.session() as sess:
out_tensor = Input(in_shape)()
from deepchem.models import TensorGraph
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import deepchem as dc
import tensorflow as tf
from deepchem.models.tensorgraph.layers import Layer, Input, Reshape, Flatten
from deepchem.models.tensorgraph.layers import Dense, SoftMaxCrossEntropy, ReduceMean, SoftMax
train = dc.data.NumpyDataset(mnist.train.images, mnist.train.labels)
valid = dc.data.NumpyDataset(mnist.validation.images, mnist.validation.labels)
tg = dc.models.TensorGraph(tensorboard=True,
model_dir='/tmp/mnist',
batch_size=1000)
feature = Input(shape=(None, 784))
tg.add_feature(feature)
# Images are square 28x28 (batch, height, width, channel)
make_image = Reshape(shape=(-1, 28, 28, 1))
tg.add_layer(make_image, parents=[feature])
class Conv2d(Layer):
def __init__(self, num_outputs, kernel_size=5, **kwargs):
self.num_outputs = num_outputs
self.kernel_size = kernel_size
super().__init__(**kwargs)
def __call__(self, *parents):
parent_tensor = parents[0].out_tensor
