Loading deepchem/metrics/__init__.py +26 −0 Original line number Diff line number Diff line Loading @@ -12,6 +12,8 @@ from sklearn.metrics import mean_absolute_error from sklearn.metrics import precision_score from sklearn.metrics import precision_recall_curve from sklearn.metrics import auc from sklearn.metrics import jaccard_similarity_score from sklearn.metrics import f1_score from scipy.stats import pearsonr Loading Loading @@ -78,6 +80,30 @@ def pearson_r2_score(y, y_pred): return pearsonr(y, y_pred)[0]**2 def jaccard_index(y, y_pred): """Computes Jaccard Index which is the Intersection Over Union metric which is commonly used in image segmentation tasks Parameters ---------- y: ground truth array y_pred: predicted array """ return jaccard_similarity_score(y, y_pred) def pixel_error(y, y_pred): """defined as 1 - the maximal F-score of pixel similarity, or squared Euclidean distance between the original and the result labels. Parameters ---------- y: ground truth array y_pred: predicted array """ return 1 - f1_score(y, y_pred) def prc_auc_score(y, y_pred): """Compute area under precision-recall curve""" y = _ensure_one_hot(y) Loading deepchem/models/tensorgraph/models/unet.py 0 → 100644 +180 −0 Original line number Diff line number Diff line """ U-Net implementation U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf Ronneberger, Philipp Fischer, Thomas Brox https://arxiv.org/pdf/1505.04597.pdf """ import tensorflow as tf import deepchem as dc from deepchem.models import Sequential from deepchem.models.tensorgraph.layers import Conv2D, MaxPool2D, Conv2DTranspose, Concat, Feature from deepchem.models import TensorGraph class UNet(TensorGraph): """ U-Net architecture implementation. Parameters ---------- img_rows : int number of rows of the image. img_cols: int number of columns of the image filters: list of input List of 5 integers to be given which will be the size of the filters in each conv layer. """ def __init__(self, img_rows=512, img_cols=512, filters=[64, 128, 256, 512, 1024], model=dc.models.TensorGraph(), **kwargs): super(UNet, self).__init__(use_queue=False, **kwargs) self.img_cols = img_cols self.img_rows = img_rows self.filters = filters self.model = dc.models.TensorGraph() input = Feature(shape=(None, self.img_rows, self.img_cols)) conv1 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[input]) conv1 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[conv1]) pool1 = MaxPool2D(ksize=2, in_layers=[conv1]) conv2 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[pool1]) conv2 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[conv2]) pool2 = MaxPool2D(ksize=2, in_layers=[conv2]) conv3 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[pool2]) conv3 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[conv3]) pool3 = MaxPool2D(ksize=2, in_layers=[conv3]) conv4 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[pool3]) conv4 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[conv4]) pool4 = MaxPool2D(ksize=2, in_layers=[conv4]) conv5 = Conv2D( num_outputs=self.filters[4], kernel_size=3, activation='relu', padding='same', in_layers=[pool4]) conv5 = Conv2D( num_outputs=self.filters[4], kernel_size=3, activation='relu', padding='same', in_layers=[conv5]) up6 = Conv2DTranspose( num_outputs=self.filters[3], kernel_size=2, in_layers=[conv5]) concat6 = Concat(in_layers=[conv4, up6], axis=1) conv6 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[concat6]) conv6 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[conv6]) up7 = Conv2DTranspose( num_outputs=self.filters[2], kernel_size=2, in_layers=[conv6]) concat7 = Concat(in_layers=[conv3, up7], axis=1) conv7 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[concat7]) conv7 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[conv7]) up8 = Conv2DTranspose( num_outputs=self.filters[1], kernel_size=2, in_layers=[conv7]) concat8 = Concat(in_layers=[conv2, up8], axis=1) conv8 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[concat8]) conv8 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[conv8]) up9 = Conv2DTranspose( num_outputs=self.filters[0], kernel_size=2, in_layers=[conv8]) concat9 = Concat(in_layers=[conv1, up9], axis=1) conv9 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[concat9]) conv9 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[conv9]) conv10 = Conv2D( num_outputs=1, kernel_size=1, activation='sigmoid', in_layers=[conv9]) model.add_output(conv10) Loading
deepchem/metrics/__init__.py +26 −0 Original line number Diff line number Diff line Loading @@ -12,6 +12,8 @@ from sklearn.metrics import mean_absolute_error from sklearn.metrics import precision_score from sklearn.metrics import precision_recall_curve from sklearn.metrics import auc from sklearn.metrics import jaccard_similarity_score from sklearn.metrics import f1_score from scipy.stats import pearsonr Loading Loading @@ -78,6 +80,30 @@ def pearson_r2_score(y, y_pred): return pearsonr(y, y_pred)[0]**2 def jaccard_index(y, y_pred): """Computes Jaccard Index which is the Intersection Over Union metric which is commonly used in image segmentation tasks Parameters ---------- y: ground truth array y_pred: predicted array """ return jaccard_similarity_score(y, y_pred) def pixel_error(y, y_pred): """defined as 1 - the maximal F-score of pixel similarity, or squared Euclidean distance between the original and the result labels. Parameters ---------- y: ground truth array y_pred: predicted array """ return 1 - f1_score(y, y_pred) def prc_auc_score(y, y_pred): """Compute area under precision-recall curve""" y = _ensure_one_hot(y) Loading
deepchem/models/tensorgraph/models/unet.py 0 → 100644 +180 −0 Original line number Diff line number Diff line """ U-Net implementation U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf Ronneberger, Philipp Fischer, Thomas Brox https://arxiv.org/pdf/1505.04597.pdf """ import tensorflow as tf import deepchem as dc from deepchem.models import Sequential from deepchem.models.tensorgraph.layers import Conv2D, MaxPool2D, Conv2DTranspose, Concat, Feature from deepchem.models import TensorGraph class UNet(TensorGraph): """ U-Net architecture implementation. Parameters ---------- img_rows : int number of rows of the image. img_cols: int number of columns of the image filters: list of input List of 5 integers to be given which will be the size of the filters in each conv layer. """ def __init__(self, img_rows=512, img_cols=512, filters=[64, 128, 256, 512, 1024], model=dc.models.TensorGraph(), **kwargs): super(UNet, self).__init__(use_queue=False, **kwargs) self.img_cols = img_cols self.img_rows = img_rows self.filters = filters self.model = dc.models.TensorGraph() input = Feature(shape=(None, self.img_rows, self.img_cols)) conv1 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[input]) conv1 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[conv1]) pool1 = MaxPool2D(ksize=2, in_layers=[conv1]) conv2 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[pool1]) conv2 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[conv2]) pool2 = MaxPool2D(ksize=2, in_layers=[conv2]) conv3 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[pool2]) conv3 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[conv3]) pool3 = MaxPool2D(ksize=2, in_layers=[conv3]) conv4 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[pool3]) conv4 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[conv4]) pool4 = MaxPool2D(ksize=2, in_layers=[conv4]) conv5 = Conv2D( num_outputs=self.filters[4], kernel_size=3, activation='relu', padding='same', in_layers=[pool4]) conv5 = Conv2D( num_outputs=self.filters[4], kernel_size=3, activation='relu', padding='same', in_layers=[conv5]) up6 = Conv2DTranspose( num_outputs=self.filters[3], kernel_size=2, in_layers=[conv5]) concat6 = Concat(in_layers=[conv4, up6], axis=1) conv6 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[concat6]) conv6 = Conv2D( num_outputs=self.filters[3], kernel_size=3, activation='relu', padding='same', in_layers=[conv6]) up7 = Conv2DTranspose( num_outputs=self.filters[2], kernel_size=2, in_layers=[conv6]) concat7 = Concat(in_layers=[conv3, up7], axis=1) conv7 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[concat7]) conv7 = Conv2D( num_outputs=self.filters[2], kernel_size=3, activation='relu', padding='same', in_layers=[conv7]) up8 = Conv2DTranspose( num_outputs=self.filters[1], kernel_size=2, in_layers=[conv7]) concat8 = Concat(in_layers=[conv2, up8], axis=1) conv8 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[concat8]) conv8 = Conv2D( num_outputs=self.filters[1], kernel_size=3, activation='relu', padding='same', in_layers=[conv8]) up9 = Conv2DTranspose( num_outputs=self.filters[0], kernel_size=2, in_layers=[conv8]) concat9 = Concat(in_layers=[conv1, up9], axis=1) conv9 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[concat9]) conv9 = Conv2D( num_outputs=self.filters[0], kernel_size=3, activation='relu', padding='same', in_layers=[conv9]) conv10 = Conv2D( num_outputs=1, kernel_size=1, activation='sigmoid', in_layers=[conv9]) model.add_output(conv10)
