for epoch in xrange(num_epochs):
print epoch
progress_filename_train = '/data/home/jianping/Desktop/adensine/cnn/batch200/log_files/train/drug_like_train-' + str(
epoch) + neural_net_present + '_' + test_type + '.csv'
progress_filename_val = '/data/home/jianping/Desktop/adenosine/cnn/batch200/log_files/val/drug_like_val-' + str(
epoch) + neural_net_present + '_' + test_type + '.csv'
#OUTPUT_train = open(progress_filename_train, 'a')
output_train_df = pd.DataFrame()
output_val_df = pd.DataFrame()
# output_test_df['structure'] = []
# output_test_df['y_value'] = []
# output_test_df['test_prediction'] = []
#this function makes a list of lists that is the minibatch
expr_list_of_lists = seqHelper.gen_batch_list_of_lists(
train_list, batch_size, (random_seed + epoch))
#print len(train_list),batch_size,(random_seed+epoch)
# print expr_list_of_lists
#then loop through the minibatches
#print len(expr_list_of_lists)
for counter, experiment_list in enumerate(expr_list_of_lists):
temp_train_df = pd.DataFrame()
#print counter, experiment_list
x_atom,x_bonds,x_atom_index,x_bond_index,x_mask,y_val = seqHelper.gen_batch_XY_reg(experiment_list,\
smiles_to_measurement,smiles_to_atom_info,smiles_to_bond_info,\
smiles_to_atom_neighbors,smiles_to_bond_neighbors,smiles_to_atom_mask)
train_prediction, train_cost, train_acc = train_func(
x_atom, x_bonds, x_atom_index, x_bond_index, x_mask, y_val)
temp_train_df['structure'] = experiment_list
test_predicition = test_predicition.flatten()
test_cost = lasagne.objectives.squared_error(target_vals, test_predicition)
#get my theano funtions
train_func = theano.function([input_molecules,input_molecules_mask,\
target_vals], [train_prediction,train_cost,visual_predictions_train], updates=updates, allow_input_downcast=True)
test_func = theano.function([input_molecules,input_molecules_mask,\
target_vals],[test_predicition,test_cost,visual_predictions_test], allow_input_downcast=True)
print "compiled functions"
#now run through all my epochs
for epoch in xrange(num_epochs):
#generate my training minibatches
expr_list_of_lists_train = seqHelper.gen_batch_list_of_lists(
train_list, batch_size, (epoch + random_seed))
for experiment_list in expr_list_of_lists_train:
_,x_vals,x_mask,y_vals, = seqHelper.gen_batch_XY_rnn(experiment_list,name_to_sequence,\
name_to_measurement,max_seq_length,alphabet_to_one_hot_sequence,output_dim)
#then do the training
train_prediction, train_error, train_viz = train_func(
x_vals, x_mask, y_vals)
test_error_list = []
if epoch % 1 == 0:
#generate my minibatches
expr_list_of_lists_test = seqHelper.gen_batch_list_of_lists(
#then define my theano functions for train and test
train_func = theano.function([input_atom,input_bonds,input_atom_index,\
input_bond_index,input_mask,target_vals], [train_prediction,train_cost,visual_predictions_train],\
updates=updates, allow_input_downcast=True)
test_func = theano.function([input_atom,input_bonds,input_atom_index,\
input_bond_index,input_mask,target_vals], [test_predicition,test_cost,visual_predictions_test], allow_input_downcast=True)
print "compiled functions"
#then run through my epochs
for epoch in xrange(num_epochs):
#get my minibatch
expr_list_of_lists_train = seqHelper.gen_batch_list_of_lists(train_list,batch_size,(random_seed+epoch))
#run through my training minibatches
for counter,experiment_list in enumerate(expr_list_of_lists_train):
x_atom,x_bonds,x_atom_index,x_bond_index,x_mask,y_val = seqHelper.gen_batch_XY_reg(experiment_list,\
smiles_to_measurement,smiles_to_atom_info,smiles_to_bond_info,\
smiles_to_atom_neighbors,smiles_to_bond_neighbors,smiles_to_atom_mask)
train_prediction,train_error,train_viz = train_func(x_atom,x_bonds,x_atom_index,x_bond_index,x_mask,y_val)
test_error_list = []
if epoch % 1 == 0:
expr_list_of_lists_test = seqHelper.gen_batch_list_of_lists(test_list,batch_size,(random_seed+epoch))
#then run through the test data