Python TensorGraph Examples

Programming Language: Python

Namespace/Package Name: deepchem.models

Class/Type: TensorGraph

Examples at hotexamples.com: 60

The TensorGraph class in DeepChem is a powerful tool for building and training neural networks. It is a part of the deepchem.models module, which is a library of pre-built models that can be customized and trained for specific tasks.

Here are a couple of examples of how to use TensorGraph in Python:

Example 1: Building a simple neural network to classify images

import deepchem as dc
import tensorflow as tf

# Load a dataset of images and labels
dataset = dc.datasets.ImageDataset(npzfile=filename)

# Define the architecture of the neural network
tg = dc.models.TensorGraph(batch_size=50)
tg.add_feature(dc.feat.RawImage())
tg.add_layer(tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu'))
tg.add_layer(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
tg.add_layer(tf.keras.layers.Flatten())
tg.add_layer(tf.keras.layers.Dense(10, activation='softmax'))

# Train the network on the dataset
tg.fit(dataset, nb_epoch=10)

In this example, we're using the ImageDataset from DeepChem to load a dataset of images and labels. Then we're defining a simple neural network architecture using TensorGraph, with one convolutional layer, one max pooling layer, and one fully connected layer. We're training the network on the dataset for 10 epochs.

Example 2: Building a graph convolutional neural network to predict a property of molecules

import deepchem as dc
import numpy as np

# Load a dataset of molecules and their properties
dataset = dc.datasets.NumpyDataset(x=np.load('features.npy'), y=np.load('properties.npy'))

# Define the architecture of the neural network
tg = dc.models.TensorGraph(batch_size=50)
tg.add_feature(dc.feat.ConvMol())
tg.add_layer(dc.nn.GraphConv(64, activation='relu'))
tg.add_layer(dc.nn.GraphPool())
tg.add_layer(dc.nn.GraphConv(64, activation='relu'))
tg.add_layer(dc.nn.Dense(1))

# Train the network on the dataset
tg.fit(dataset, nb_epoch=50)

In this example, we're using the NumpyDataset from DeepChem to load a dataset of molecules and their properties. Then we're defining a graph convolutional neural network architecture using TensorGraph, with two graph convolutional layers and one fully connected layer. We're training the network on the dataset for 50 epochs. Overall, TensorGraph is a versatile tool for building and training neural networks, and it offers a lot of flexibility for customization. The TensorGraph class belongs to the DeepChem package library, which is a package specifically designed for deep learning in molecules and materials science.

Python TensorGraph - 60 examples found. These are the top rated real world Python examples of deepchem.models.TensorGraph extracted from open source projects. You can rate examples to help us improve the quality of examples.

Frequently Used Methods

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save(30)

TensorGraph(30)

build(15)

add_output(14)

set_loss(10)

load_from_dir(6)

_add_layer(2)

_get_tf(2)

set_optimizer(2)

Example #1

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_Gather_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = Gather(indices=[[0], [2], [3]], in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #2

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_Repeat_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = Repeat(n_times=10, in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #3

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def test_Cast_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = Cast(in_layers=feature, dtype=tf.int32)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #4

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def test_GRU_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 10, 10))
    layer = GRU(n_hidden=10, batch_size=tg.batch_size, in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #5

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_DTNNEmbedding_pickle():
    tg = TensorGraph()
    atom_numbers = Feature(shape=(None, 23), dtype=tf.int32)
    Embedding = DTNNEmbedding(in_layers=[atom_numbers])
    tg.add_output(Embedding)
    tg.set_loss(Embedding)
    tg.build()
    tg.save()

Example #6

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_BatchNorm_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 10))
    layer = BatchNorm(in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #7

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def test_SoftmaxCrossEntropy_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = SoftMaxCrossEntropy(in_layers=[feature, feature])
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #8

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_Reshape_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = Reshape(shape=(None, 2), in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #9

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def test_ReduceSquareDifference_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = ReduceSquareDifference(in_layers=[feature, feature])
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #10

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def test_ToFloat_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = ToFloat(in_layers=[feature])
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #11

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def test_Dense_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    dense = Dense(out_channels=1, in_layers=feature)
    tg.add_output(dense)
    tg.set_loss(dense)
    tg.build()
    tg.save()

Example #12

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def test_Conv3DTranspose_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 10, 10, 10, 1))
    layer = Conv3DTranspose(num_outputs=3, in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #13

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def test_Slice_pickle():
    V = Feature(shape=(None, 10))
    out = Slice(5, 1, in_layers=[V])
    tg = TensorGraph()
    tg.add_output(out)
    tg.set_loss(out)
    tg.build()
    tg.save()

Example #14

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_CombineMeanStd_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = CombineMeanStd(in_layers=[feature, feature])
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #15

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def test_Exp_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = Exp(feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #16

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_Transpose_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = Transpose(perm=(1, 0), in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #17

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def test_StopGradient_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    output = StopGradient(feature)
    tg.add_output(output)
    tg.set_loss(output)
    tg.build()
    tg.save()

Example #18

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_DTNNExtract_pickle():
    tg = TensorGraph()
    atom_features = Feature(shape=(None, 30))
    Ext = DTNNExtract(0, in_layers=[atom_features])
    tg.add_output(Ext)
    tg.set_loss(Ext)
    tg.build()
    tg.save()

Example #19

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def test_Conv1D_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1, 1))
    conv = Conv1D(2, 1, in_layers=feature)
    tg.add_output(conv)
    tg.set_loss(conv)
    tg.build()
    tg.save()

Example #20

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File: test_layers_pickle.py Project: JYeop/deepchem

def test_WeightedError_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 10))
    layer = WeightedError(in_layers=[feature, feature])
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #21

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def test_Softmax_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = SoftMax(in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #22

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def test_Squeeze_pickle():
    tg = TensorGraph()
    feature = Feature(shape=(tg.batch_size, 1))
    layer = Squeeze(squeeze_dims=-1, in_layers=feature)
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #23

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File: test_layers_pickle.py Project: zzachw/deepchem

def test_hingeloss_pickle():
  tg = TensorGraph()
  feature = Feature(shape=(1, None))
  layer = HingeLoss(in_layers=[feature, feature])
  tg.add_output(layer)
  tg.set_loss(layer)
  tg.build()
  tg.save()

Example #24

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def testGraphCNNPoolLayer_pickle():
  V = Feature(shape=(None, 200, 50))
  A = Feature(shape=(None, 200, 1, 200))
  gcnnpool = GraphEmbedPoolLayer(32, in_layers=[V, A])
  tg = TensorGraph()
  tg.add_output(gcnnpool)
  tg.set_loss(gcnnpool)
  tg.build()
  tg.save()

Example #25

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def testGraphCNN_pickle():
  V = Feature(shape=(None, 200, 50))
  A = Feature(shape=(None, 200, 1, 200))
  gcnn = GraphCNN(32, in_layers=[V, A])
  tg = TensorGraph()
  tg.add_output(gcnn)
  tg.set_loss(gcnn)
  tg.build()
  tg.save()

Example #26

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def test_SetGather_pickle():
  tg = TensorGraph()
  atom_feature = Feature(shape=(None, 100))
  atom_split = Feature(shape=(None,), dtype=tf.int32)
  Gather = SetGather(5, 16, in_layers=[atom_feature, atom_split])
  tg.add_output(Gather)
  tg.set_loss(Gather)
  tg.build()
  tg.save()

Example #27

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def test_DAGGather_pickle():
  tg = TensorGraph()
  atom_features = Feature(shape=(None, 30))
  membership = Feature(shape=(None,), dtype=tf.int32)
  Gather = DAGGather(in_layers=[atom_features, membership])
  tg.add_output(Gather)
  tg.set_loss(Gather)
  tg.build()
  tg.save()

Example #28

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def test_SparseSoftmaxCrossEntropy_pickle():
    tg = TensorGraph()
    logits = Feature(shape=(tg.batch_size, 5))
    labels = Feature(shape=(tg.batch_size, ), dtype=tf.int32)
    layer = SparseSoftMaxCrossEntropy(in_layers=[labels, logits])
    tg.add_output(layer)
    tg.set_loss(layer)
    tg.build()
    tg.save()

Example #29

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def test_Constant_pickle():
  tg = TensorGraph()
  feature = Feature(shape=(tg.batch_size, 1))
  layer = Constant(np.array([15.0]))
  output = Add(in_layers=[feature, layer])
  tg.add_output(output)
  tg.set_loss(output)
  tg.build()
  tg.save()

Example #30

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def test_Variable_pickle():
  tg = TensorGraph()
  feature = Feature(shape=(tg.batch_size, 1))
  layer = Variable(np.array([15.0]))
  output = Multiply(in_layers=[feature, layer])
  tg.add_output(output)
  tg.set_loss(output)
  tg.build()
  tg.save()

Example #31

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