Remove tf-mhcflurry.py (not necessary) and clear TF exploration notebook state.

script/tf-mhcflurry.py

-from mhcflurry.dataset import Dataset
-from mhcflurry.peptide_encoding import indices_to_hotshot_encoding
-
-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_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)) # TF backend segfaults here
-model.add(Activation("sigmoid"))
-model.compile(loss="mse", optimizer="rmsprop")
-
-model.fit(training_hotshot, df_kmers[1], nb_epoch=5, batch_size=1)