def create_discriminator(self, data_inputs, conditional_inputs):
discrim_in = layers.Concat(data_inputs + conditional_inputs)
dense = layers.Dense(10,
in_layers=discrim_in,
activation_fn=tf.nn.relu)
return layers.Dense(1,
in_layers=dense,
activation_fn=tf.sigmoid)
def test_concat(self):
"""Test invoking Concat in eager mode."""
with context.eager_mode():
input1 = np.random.rand(5, 10).astype(np.float32)
input2 = np.random.rand(5, 4).astype(np.float32)
result = layers.Concat()(input1, input2)
assert result.shape == (5, 14)
assert np.array_equal(input1, result[:, :10])
assert np.array_equal(input2, result[:, 10:])
def create_generator(self, noise_input, conditional_inputs): gen_in = layers.Concat([noise_input] + conditional_inputs) return [layers.Dense(1, in_layers=gen_in)]
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,
stride=2,
in_layers=concat2)
concat3 = layers.Concat(in_layers=[conv1, deconv2], axis=3)
deconv3 = layers.Conv2DTranspose(1, kernel_size=5, stride=2, in_layers=concat3)
# Compute the final output.
concat4 = layers.Concat(in_layers=[features, deconv3], axis=3)
logits = layers.Conv2D(1,
kernel_size=5,
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])