Simple script that runs on Theano but not TF backend

script/tf-mhcflurry.py

+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+from mhcflurry.dataset import Dataset
+from mhcflurry.peptide_encoding import indices_to_hotshot_encoding
+
+allele_list=['HLA-A0201', 'HLA-A2301', 'HLA-A2402', 'HLA-A1101']
+
+file_to_explore="/root/.local/share/mhcflurry/2/class1_data/combined_human_class1_dataset.csv"
+dataset = Dataset.from_csv(
+        filename=file_to_explore,
+        sep=",",
+        peptide_column_name="peptide")
+df = dataset.to_dataframe()
+df_reduced = df[df.allele.isin(allele_list)][['allele','affinity']].reset_index(drop=True)
+df_kmers = dataset.kmer_index_encoding()
+training_hotshot = indices_to_hotshot_encoding(df_kmers[0])
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+model = Sequential()
+model.add(Dense(input_dim=9*21, output_dim=1))
+model.add(Activation("sigmoid"))
+model.compile(loss="mse", optimizer="rmsprop")
+
+model.fit(training_hotshot, df_kmers[1], nb_epoch=5, batch_size=1)
+