1 files changed, 73 insertions, 0 deletions
diff --git a/lstm_chem/model.py b/lstm_chem/model.py
new file mode 100755
index 0000000..079589a
--- /dev/null
+++ b/lstm_chem/model.py
@@ -0,0 +1,73 @@
+import os
+import time
+from tensorflow.keras import Sequential
+from tensorflow.keras.models import model_from_json
+from tensorflow.keras.layers import LSTM, Dense
+from tensorflow.keras.initializers import RandomNormal
+from lstm_chem.utils.smiles_tokenizer import SmilesTokenizer
+
+
+class LSTMChem(object):
+    def __init__(self, config, session='train'):
+        assert session in ['train', 'generate', 'finetune'], \
+                'one of {train, generate, finetune}'
+
+        self.config = config
+        self.session = session
+        self.model = None
+
+        if self.session == 'train':
+            self.build_model()
+        else:
+            self.model = self.load(self.config.model_arch_filename,
+                                   self.config.model_weight_filename)
+
+    def build_model(self):
+        st = SmilesTokenizer()
+        n_table = len(st.table)
+        weight_init = RandomNormal(mean=0.0,
+                                   stddev=0.05,
+                                   seed=self.config.seed)
+
+        self.model = Sequential()
+        self.model.add(
+            LSTM(units=self.config.units,
+                 input_shape=(None, n_table),
+                 return_sequences=True,
+                 kernel_initializer=weight_init,
+                 dropout=0.3))
+        self.model.add(
+            LSTM(units=self.config.units,
+                 input_shape=(None, n_table),
+                 return_sequences=True,
+                 kernel_initializer=weight_init,
+                 dropout=0.5))
+        self.model.add(
+            Dense(units=n_table,
+                  activation='softmax',
+                  kernel_initializer=weight_init))
+
+        arch = self.model.to_json(indent=2)
+        self.config.model_arch_filename = os.path.join(self.config.exp_dir,
+                                                       'model_arch.json')
+        with open(self.config.model_arch_filename, 'w') as f:
+            f.write(arch)
+
+        self.model.compile(optimizer=self.config.optimizer,
+                           loss='categorical_crossentropy')
+
+    def save(self, checkpoint_path):
+        assert self.model, 'You have to build the model first.'
+
+        print('Saving model ...')
+        self.model.save_weights(checkpoint_path)
+        print('model saved.')
+
+    def load(self, model_arch_file, checkpoint_file):
+        print(f'Loading model architecture from {model_arch_file} ...')
+        with open(model_arch_file) as f:
+            model = model_from_json(f.read())
+        print(f'Loading model checkpoint from {checkpoint_file} ...')
+        model.load_weights(checkpoint_file)
+        print('Loaded the Model.')
+        return model