Ich wollte Deep Learning mit einer Verbindung starten, also habe ich beschlossen, DeepChems GraphConvModel zu hacken und in Keras zu implementieren. Daher habe ich mich zunächst entschlossen, das, was in Keras implementiert ist, mit der Zusammenfassungsmethode des Modellobjekts auszugeben.
Setzen Sie model.summary () in Zeile 624 der Datei, die die GraphConvModel-Klasse definiert, und erstellen Sie ein Vorhersagemodell mit den entsprechenden Daten.
<anaconda3>/envs/deepchem/lib/python3.7/site-packages/deepchem/models/graph_conv.py
print(model.summary())
So was. Ich lese die Zeitung und bekomme eine allgemeine Vorstellung, aber DeepChem unterscheidet sich ein wenig von der Zeitung, und ich werde sie von nun an analysieren.
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 75)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 2)] 0
__________________________________________________________________________________________________
input_3 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
input_6 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
input_7 (InputLayer) [(None, 2)] 0
__________________________________________________________________________________________________
input_8 (InputLayer) [(None, 3)] 0
__________________________________________________________________________________________________
input_9 (InputLayer) [(None, 4)] 0
__________________________________________________________________________________________________
input_10 (InputLayer) [(None, 5)] 0
__________________________________________________________________________________________________
input_11 (InputLayer) [(None, 6)] 0
__________________________________________________________________________________________________
input_12 (InputLayer) [(None, 7)] 0
__________________________________________________________________________________________________
input_13 (InputLayer) [(None, 8)] 0
__________________________________________________________________________________________________
input_14 (InputLayer) [(None, 9)] 0
__________________________________________________________________________________________________
input_15 (InputLayer) [(None, 10)] 0
__________________________________________________________________________________________________
input_16 (InputLayer) [(None, 11)] 0
__________________________________________________________________________________________________
graph_conv (GraphConv) (None, 64) 102144 input_1[0][0]
input_2[0][0]
input_3[0][0]
input_6[0][0]
input_7[0][0]
input_8[0][0]
input_9[0][0]
input_10[0][0]
input_11[0][0]
input_12[0][0]
input_13[0][0]
input_14[0][0]
input_15[0][0]
input_16[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 64) 256 graph_conv[0][0]
__________________________________________________________________________________________________
graph_pool (GraphPool) (None, 64) 0 batch_normalization[0][0]
input_2[0][0]
input_3[0][0]
input_6[0][0]
input_7[0][0]
input_8[0][0]
input_9[0][0]
input_10[0][0]
input_11[0][0]
input_12[0][0]
input_13[0][0]
input_14[0][0]
input_15[0][0]
input_16[0][0]
__________________________________________________________________________________________________
graph_conv_1 (GraphConv) (None, 64) 87360 graph_pool[0][0]
input_2[0][0]
input_3[0][0]
input_6[0][0]
input_7[0][0]
input_8[0][0]
input_9[0][0]
input_10[0][0]
input_11[0][0]
input_12[0][0]
input_13[0][0]
input_14[0][0]
input_15[0][0]
input_16[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 64) 256 graph_conv_1[0][0]
__________________________________________________________________________________________________
graph_pool_1 (GraphPool) (None, 64) 0 batch_normalization_1[0][0]
input_2[0][0]
input_3[0][0]
input_6[0][0]
input_7[0][0]
input_8[0][0]
input_9[0][0]
input_10[0][0]
input_11[0][0]
input_12[0][0]
input_13[0][0]
input_14[0][0]
input_15[0][0]
input_16[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 128) 8320 graph_pool_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 128) 512 dense[0][0]
__________________________________________________________________________________________________
graph_gather (GraphGather) (64, 256) 0 batch_normalization_2[0][0]
input_2[0][0]
input_3[0][0]
input_6[0][0]
input_7[0][0]
input_8[0][0]
input_9[0][0]
input_10[0][0]
input_11[0][0]
input_12[0][0]
input_13[0][0]
input_14[0][0]
input_15[0][0]
input_16[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (64, 2) 514 graph_gather[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (64, 1, 2) 0 dense_1[0][0]
__________________________________________________________________________________________________
input_4 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
trim_graph_output (TrimGraphOut (None, 1, 2) 0 reshape[0][0]
input_4[0][0]
__________________________________________________________________________________________________
input_5 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
softmax (Softmax) (None, 1, 2) 0 trim_graph_output[0][0]
==================================================================================================
Total params: 199,362
Trainable params: 198,850
Non-trainable params: 512
__________________________________________________________________________________________________