def test_tensorboard(self): """Test creating an Estimator from a TensorGraph that logs information to TensorBoard.""" n_samples = 10 n_features = 3 n_tasks = 2 # Create a dataset and an input function for processing it. np.random.seed(123) X = np.random.rand(n_samples, n_features) y = np.zeros((n_samples, n_tasks)) dataset = dc.data.NumpyDataset(X, y) def input_fn(epochs): x, y, weights = dataset.make_iterator(batch_size=n_samples, epochs=epochs).get_next() return {'x': x, 'weights': weights}, y # Create a TensorGraph model. model = dc.models.TensorGraph() features = layers.Feature(shape=(None, n_features)) dense = layers.Dense(out_channels=n_tasks, in_layers=features) dense.set_summary('histogram') model.add_output(dense) labels = layers.Label(shape=(None, n_tasks)) loss = layers.ReduceMean(layers.L2Loss(in_layers=[labels, dense])) model.set_loss(loss) # Create an estimator from it. x_col = tf.feature_column.numeric_column('x', shape=(n_features, )) estimator = model.make_estimator(feature_columns=[x_col]) # Train the model. estimator.train(input_fn=lambda: input_fn(100))
def __init__(self, input_tokens, output_tokens, max_output_length, encoder_layers=4, decoder_layers=4, embedding_dimension=512, dropout=0.0, reverse_input=True, variational=False, annealing_start_step=5000, annealing_final_step=10000, **kwargs): """Construct a SeqToSeq model. In addition to the following arguments, this class also accepts all the keyword arguments from TensorGraph. Parameters ---------- input_tokens: list a list of all tokens that may appear in input sequences output_tokens: list a list of all tokens that may appear in output sequences max_output_length: int the maximum length of output sequence that may be generated encoder_layers: int the number of recurrent layers in the encoder decoder_layers: int the number of recurrent layers in the decoder embedding_dimension: int the width of the embedding vector. This also is the width of all recurrent layers. dropout: float the dropout probability to use during training reverse_input: bool if True, reverse the order of input sequences before sending them into the encoder. This can improve performance when working with long sequences. variational: bool if True, train the model as a variational autoencoder. This adds random noise to the encoder, and also constrains the embedding to follow a unit Gaussian distribution. annealing_start_step: int the step (that is, batch) at which to begin turning on the constraint term for KL cost annealing annealing_final_step: int the step (that is, batch) at which to finish turning on the constraint term for KL cost annealing """ super(SeqToSeq, self).__init__( use_queue=False, **kwargs) # TODO can we make it work with the queue? if SeqToSeq.sequence_end not in input_tokens: input_tokens = input_tokens + [SeqToSeq.sequence_end] if SeqToSeq.sequence_end not in output_tokens: output_tokens = output_tokens + [SeqToSeq.sequence_end] self._input_tokens = input_tokens self._output_tokens = output_tokens self._input_dict = dict((x, i) for i, x in enumerate(input_tokens)) self._output_dict = dict((x, i) for i, x in enumerate(output_tokens)) self._max_output_length = max_output_length self._embedding_dimension = embedding_dimension self._annealing_final_step = annealing_final_step self._annealing_start_step = annealing_start_step self._features = self._create_features() self._labels = layers.Label(shape=(None, None, len(output_tokens))) self._gather_indices = layers.Feature( shape=(self.batch_size, 2), dtype=tf.int32) self._reverse_input = reverse_input self._variational = variational self.embedding = self._create_encoder(encoder_layers, dropout) self.output = self._create_decoder(decoder_layers, dropout) self.set_loss(self._create_loss()) self.add_output(self.output)
# Train a model to predict how well sequences will work for RNA interference. import deepchem as dc import deepchem.models.tensorgraph.layers as layers import tensorflow as tf import matplotlib.pyplot as plot # Build the model. model = dc.models.TensorGraph(model_dir='rnai') features = layers.Feature(shape=(None, 21, 4)) labels = layers.Label(shape=(None, 1)) prev = features for i in range(2): prev = layers.Conv1D(filters=10, kernel_size=10, activation=tf.nn.relu, padding='same', in_layers=prev) prev = layers.Dropout(dropout_prob=0.3, in_layers=prev) output = layers.Dense(out_channels=1, activation_fn=tf.sigmoid, in_layers=layers.Flatten(prev)) model.add_output(output) loss = layers.ReduceMean(layers.L2Loss(in_layers=[labels, output])) model.set_loss(loss) # Load the data. train = dc.data.DiskDataset('train_siRNA') valid = dc.data.DiskDataset('valid_siRNA')
for row, blur in zip(rows, blurs): fname = f.replace('_F1', '_F%d' % blur).replace('_A', '_%s' % row) files.append(os.path.join(image_dir, fname)) labels.append(os.path.join(label_dir, f)) loader = dc.data.ImageLoader() dataset = loader.featurize(files, labels) splitter = dc.splits.RandomSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset, seed=123) # Create the model. learning_rate = dc.models.optimizers.ExponentialDecay(0.01, 0.9, 250) model = dc.models.TensorGraph(learning_rate=learning_rate, model_dir='models/segmentation') features = layers.Feature(shape=(None, 520, 696, 1)) / 255.0 labels = layers.Label(shape=(None, 520, 696, 1)) / 255.0 # Downsample three times. conv1 = layers.Conv2D(16, kernel_size=5, stride=2, in_layers=features) conv2 = layers.Conv2D(32, kernel_size=5, stride=2, in_layers=conv1) conv3 = layers.Conv2D(64, kernel_size=5, stride=2, in_layers=conv2) # Do a 1x1 convolution. conv4 = layers.Conv2D(64, kernel_size=1, stride=1, in_layers=conv3) # Upsample three times. concat1 = layers.Concat(in_layers=[conv3, conv4], axis=3) deconv1 = layers.Conv2DTranspose(32, kernel_size=5, stride=2, in_layers=concat1) concat2 = layers.Concat(in_layers=[conv2, deconv1], axis=3) deconv2 = layers.Conv2DTranspose(16, kernel_size=5,
from tensorflow.examples.tutorials.mnist import input_data # Read dataset mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # Transfor dataset into format readable by deepchem train_dataset = dc.data.NumpyDataset(mnist.train.images, mnist.train.labels) test_dataset = dc.data.NumpyDataset(mnist.test.images, mnist.test.labels) model = dc.models.TensorGraph(model_dir='mnist') # Images in MNIST are 28x28, flattened 784 # None means that the input can be of any dimension - we can use it as variable batch size feature = layers.Feature(shape=(None, 784)) # 0..9 digits label = layers.Label(shape=(None, 10)) # Reshape flattened layer to matrix to use it with convolution make_image = layers.Reshape(shape=(None, 28, 28), in_layers=feature) conv2d_1 = layers.Conv2D(num_outputs=32, activation_fn=tf.nn.relu, in_layers=make_image) conv2d_2 = layers.Conv2D(num_outputs=64, activation_fn=tf.nn.relu, in_layers=conv2d_1) flatten = layers.Flatten(in_layers=conv2d_2) dense1 = layers.Dense(out_channels=1024, activation_fn=tf.nn.relu, in_layers=flatten) dense2 = layers.Dense(out_channels=10, activation_fn=None, in_layers=dense1)
def create_model(): """ Create our own MNIST model from scratch :return: :rtype: """ mnist = input_data.read_data_sets("MNIST_DATA/", one_hot=True) # the layers from deepchem are the building blocks of what we will use to make our deep learning architecture # now we wrap our dataset into a NumpyDataset train_dataset = dc.data.NumpyDataset(mnist.train.images, mnist.train.labels) test_dataset = dc.data.NumpyDataset(mnist.test.images, mnist.test.labels) # we will create a model that will take an input, add multiple layers, where each layer takes input from the # previous layers. model = dc.models.TensorGraph(model_dir='mnist') # 784 corresponds to an image of size 28 X 28 # 10 corresponds to the fact that there are 10 possible digits (0-9) # the None indicates that we can accept any size input (e.g. an empty array or 500 items each with 784 features) # our data is also categorical so we must one hot encode, set single array element to 1 and the rest to 0 feature = layers.Feature(shape=(None, 784)) labels = layers.Label(shape=(None, 10)) # in order to apply convolutional layers to our input, we convert flat vector of 785 to 28X28 # in_layers means it takes our feature layer as an input make_image = layers.Reshape(shape=(None, 28, 28), in_layers=feature) # now that we have reshaped the input, we pass to convolution layers conv2d_1 = layers.Conv2D(num_outputs=32, activation_fn=tf.nn.relu, in_layers=make_image) conv2d_2 = layers.Conv2D(num_outputs=64, activation_fn=tf.nn.relu, in_layers=conv2d_1) # we want to end by applying fully connected (Dense) layers to the outputs of our convolutional layer # but first, we must flatten the layer from a 2d matrix to a 1d vector flatten = layers.Flatten(in_layers=conv2d_2) dense1 = layers.Dense(out_channels=1024, activation_fn=tf.nn.relu, in_layers=flatten) # note that this is final layer so out_channels of 10 represents the 10 outputs and no activation_fn dense2 = layers.Dense(out_channels=10, activation_fn=None, in_layers=dense1) # next we want to connect this output to a loss function, so we can train the output # compute the value of loss function for every sample then average of all samples to get final loss (ReduceMean) smce = layers.SoftMaxCrossEntropy(in_layers=[labels, dense2]) loss = layers.ReduceMean(in_layers=smce) model.set_loss(loss) # for MNIST we want the probability that a given sample represents one of the 10 digits # we can achieve this using a softmax function to get the probabilities, then cross entropy to get the labels output = layers.SoftMax(in_layers=dense2) model.add_output(output) # if our model takes long to train, reduce nb_epoch to 1 model.fit(train_dataset, nb_epoch=10) # our metric is accuracy, the fraction of labels that are accurately predicted metric = dc.metrics.Metric(dc.metrics.accuracy_score) train_scores = model.evaluate(train_dataset, [metric]) test_scores = model.evaluate(test_dataset, [metric]) print('train_scores', train_scores) print('test_scores', test_scores)