def test_flatten(self):
"""Test invoking Flatten in eager mode."""
with context.eager_mode():
with tfe.IsolateTest():
input = np.random.rand(5, 10, 4).astype(np.float32)
result = layers.Flatten()(input)
assert result.shape == (5, 40)
def _create_encoder(self, n_layers, dropout):
"""Create the encoder layers."""
prev_layer = self._features
for i in range(len(self._filter_sizes)):
filter_size = self._filter_sizes[i]
kernel_size = self._kernel_sizes[i]
if dropout > 0.0:
prev_layer = layers.Dropout(dropout, in_layers=prev_layer)
prev_layer = layers.Conv1D(
filters=filter_size,
kernel_size=kernel_size,
in_layers=prev_layer,
activation_fn=tf.nn.relu)
prev_layer = layers.Flatten(prev_layer)
prev_layer = layers.Dense(
self._decoder_dimension, in_layers=prev_layer, activation_fn=tf.nn.relu)
prev_layer = layers.BatchNorm(prev_layer)
if self._variational:
self._embedding_mean = layers.Dense(
self._embedding_dimension,
in_layers=prev_layer,
name='embedding_mean')
self._embedding_stddev = layers.Dense(
self._embedding_dimension, in_layers=prev_layer, name='embedding_std')
prev_layer = layers.CombineMeanStd(
[self._embedding_mean, self._embedding_stddev], training_only=True)
return prev_layer
def __init__(self,
seq_length,
use_RNN=False,
num_tasks=1,
num_filters=15,
kernel_size=15,
pool_width=35,
L1=0,
dropout=0.0,
verbose=True,
**kwargs):
super(SequenceDNN, self).__init__(**kwargs)
self.num_tasks = num_tasks
self.verbose = verbose
self.add(layers.Conv2D(num_filters, kernel_size=kernel_size))
self.add(layers.Dropout(dropout))
self.add(layers.Flatten())
self.add(layers.Dense(self.num_tasks, activation_fn=tf.nn.relu))
# 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')
# Train the model, tracking its performance on the training and validation datasets.
metric = dc.metrics.Metric(dc.metrics.pearsonr, mode='regression')
for i in range(20):
model.fit(train, nb_epoch=10)
print(model.evaluate(train, [metric])['pearsonr'][0])
import deepchem.models.tensorgraph.layers as layers
import tensorflow as tf
import numpy as np
# Build the model.
model = dc.models.TensorGraph(batch_size=1000, model_dir='chromatin')
features = layers.Feature(shape=(None, 101, 4))
accessibility = layers.Feature(shape=(None, 1))
labels = layers.Label(shape=(None, 1))
weights = layers.Weights(shape=(None, 1))
prev = features
for i in range(3):
prev = layers.Conv1D(filters=15, kernel_size=10, activation=tf.nn.relu, padding='same', in_layers=prev)
prev = layers.Dropout(dropout_prob=0.5, in_layers=prev)
prev = layers.Concat([layers.Flatten(prev), accessibility])
logits = layers.Dense(out_channels=1, in_layers=prev)
output = layers.Sigmoid(logits)
model.add_output(output)
loss = layers.SigmoidCrossEntropy(in_layers=[labels, logits])
weighted_loss = layers.WeightedError(in_layers=[loss, weights])
model.set_loss(weighted_loss)
# Load the data.
train = dc.data.DiskDataset('train_dataset')
valid = dc.data.DiskDataset('valid_dataset')
span_accessibility = {}
for line in open('accessibility.txt'):
fields = line.split()
span_accessibility[fields[0]] = float(fields[1])
# 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)
# Computes the loss for every sample
smce = layers.SoftMaxCrossEntropy(in_layers=[label, dense2])
# Average all the losses
loss = layers.ReduceMean(in_layers=smce)
model.set_loss(loss)
# Convert the output from logits to probs
output = layers.SoftMax(in_layers=dense2)
model.add_output(output)
# Build the model.
model = dc.models.TensorGraph(batch_size=1000, model_dir='tf')
features = layers.Feature(shape=(None, 101, 4))
labels = layers.Label(shape=(None, 1))
weights = layers.Weights(shape=(None, 1))
prev = features
for i in range(3):
prev = layers.Conv1D(filters=15,
kernel_size=10,
activation=tf.nn.relu,
padding='same',
in_layers=prev)
prev = layers.Dropout(dropout_prob=0.5, in_layers=prev)
logits = layers.Dense(out_channels=1, in_layers=layers.Flatten(prev))
output = layers.Sigmoid(logits)
model.add_output(output)
loss = layers.SigmoidCrossEntropy(in_layers=[labels, logits])
weighted_loss = layers.WeightedError(in_layers=[loss, weights])
model.set_loss(weighted_loss)
# Load the data.
train = dc.data.DiskDataset('train_dataset')
valid = dc.data.DiskDataset('valid_dataset')
# Train the model, tracking its performance on the training and validation datasets.
metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
for i in range(20):
files.append(os.path.join(image_dir, f))
labels.append(int(re.findall('_C(.*?)_', f)[0]))
loader = dc.data.ImageLoader()
dataset = loader.featurize(files, np.array(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.tensorgraph.optimizers.ExponentialDecay(0.001, 0.9, 250)
model = dc.models.TensorGraph(learning_rate=learning_rate, model_dir='models/model')
features = layers.Feature(shape=(None, 520, 696))
labels = layers.Label(shape=(None,))
prev_layer = features
for num_outputs in [16, 32, 64, 128, 256]:
prev_layer = layers.Conv2D(num_outputs, kernel_size=5, stride=2, in_layers=prev_layer)
output = layers.Dense(1, in_layers=layers.Flatten(prev_layer))
model.add_output(output)
loss = layers.ReduceSum(layers.L2Loss(in_layers=(output, labels)))
model.set_loss(loss)
if not os.path.exists('./models'):
os.mkdir('models')
if not os.path.exists('./models/model'):
os.mkdir('models/model')
if not RETRAIN:
model.restore()
# Train it and evaluate performance on the test set.
if RETRAIN:
print("About to fit model for 50 epochs")
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)
