added requirements.txt, small modifications to allow for Python 3 and…

… TensorFlow 2.0

deepnovo_config.py

@@ -182,7 +182,7 @@
            'V': 99.06841, # 19
           }
 
-mass_ID = [mass_AA[vocab_reverse[x]] for x in xrange(vocab_size)]
+mass_ID = [mass_AA[vocab_reverse[x]] for x in range(vocab_size)]
 mass_ID_np = np.array(mass_ID, dtype=np.float32)
 
 mass_AA_min = mass_AA["G"] # 57.02146

deepnovo_main_modules.py

@@ -37,7 +37,7 @@
 import sys
 import time
 import re
-import resource
+# import resource
 
 import numpy as np
 #~ cimport numpy as np
@@ -2481,8 +2481,8 @@ def train():
 
       #~ gc .collect()
 
-      print("RESOURCE-train_cycle: ",
-            resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1000)
+      print("RESOURCE-train_cycle")
+            # resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1000)
 
       # stop training if >= 50 epochs
       epoch = (model.global_step.eval()

deepnovo_model.py

@@ -40,8 +40,9 @@
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import rnn_cell
-from tensorflow.python.ops import rnn_cell_impl
+# from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope
+from tensorflow.contrib.rnn.python.ops import core_rnn_cell
 
 import deepnovo_config
 import deepnovo_model_training
@@ -334,7 +335,7 @@ def build_network(self, input_dict, dropout_keep):
         # linear transform to logit [128, 26]
         # TODO(nh2tran): replace _linear and remove scope
         with tf.variable_scope("output_logit"):
-          feature_logit = rnn_cell_impl._linear(args=feature,
+          feature_logit = core_rnn_cell._linear(args=feature,
                                                 output_size=self.vocab_size,
                                                 bias=True,
                                                 bias_initializer=None,#0.1,
@@ -437,7 +438,7 @@ def _build_cnn_ion(self, input_intensity, direction):
     # linear transform to logit [128, 26], in case only cnn_ion model is used
     # TODO(nh2tran): replace _linear and remove scope
     with tf.variable_scope("intensity_output_projected"):
-      cnn_ion_logit = rnn_cell_impl._linear(args=cnn_ion_feature,
+      cnn_ion_logit = core_rnn_cell._linear(args=cnn_ion_feature,
                                             output_size=self.vocab_size,
                                             bias=True,
                                             bias_initializer=None,#0.1,
@@ -616,7 +617,7 @@ def _build_lstm(self, cnn_spectrum, input_lstm_state, embedding_AAid, direction)
     # linear transform to logit [128, 26], in case only lstm model is used
     # TODO(nh2tran): replace _linear and remove scope
     with tf.variable_scope("lstm_output_projected"):
-      lstm_logit = rnn_cell_impl._linear(
+      lstm_logit = core_rnn_cell._linear(
           args=lstm_feature,
           output_size=self.vocab_size,
           bias=True,

requirements.txt

@@ -0,0 +1,7 @@
+cython
+tensorflow
+lxml
+numpy
+matplotlib
+pyteomics
+biopython