def test_errors(): # test Exception cases units = 6 batch_shape = (8, 100, 2 * units) reset_seeds(reset_graph_with_backend=K) model = make_model(GRU, batch_shape, activation='relu', recurrent_dropout=0.3, IMPORTS=IMPORTS) x, y, sw = make_data(batch_shape, units) model.train_on_batch(x, y, sw) grads = get_gradients(model, 1, x, y) grads_4D = np.expand_dims(grads, -1) from see_rnn.inspect_gen import get_layer, _make_grads_fn pass_on_error(features_0D, grads) pass_on_error(features_0D, grads_4D) pass_on_error(features_1D, grads_4D) pass_on_error(features_2D, grads_4D) pass_on_error(features_2D, grads) pass_on_error(get_gradients, model, 1, x, y, mode='cactus') pass_on_error(get_gradients, model, 1, x, y, layer=model.layers[1]) pass_on_error(_make_grads_fn, model, model.layers[1], mode='banana') pass_on_error(features_hist, grads[:, :4, :3], po='tato') pass_on_error(features_hist_v2, grads[:, :4, :3], po='tato') pass_on_error(get_layer, model) pass_on_error(get_layer, model, 'capsule') pass_on_error(rnn_heatmap, model, 1, input_data=x, labels=y, mode='coffee') pass_on_error(rnn_heatmap, model, 1, co='vid') pass_on_error(rnn_heatmap, model, 1, norm=(0, 1, 2)) pass_on_error(rnn_heatmap, model, 1, mode='grads') pass_on_error(rnn_histogram, model, 1, norm=None) pass_on_error(rnn_heatmap, model, layer_index=9001) pass_on_error(features_0D, grads, cake='lie') pass_on_error(features_1D, grads, pup='not just any') pass_on_error(features_2D, grads, true=False) outs = list(get_outputs(model, 1, x, as_dict=True).values()) pass_on_error(rnn_histogram, model, 1, data=outs) pass_on_error(rnn_histogram, model, 1, data=[1]) pass_on_error(rnn_histogram, model, 1, data=[[1]]) pass_on_error(features_hist, grads, co='vid') pass_on_error(features_0D, grads, configs={'x': {}}) pass_on_error(features_1D, grads, configs={'x': {}}) pass_on_error(features_2D, grads, configs={'x': {}}) pass_on_error(features_hist, grads, configs={'x': {}}) pass_on_error(features_hist_v2, grads, configs={'x': {}}) cprint("\n<< EXCEPTION TESTS PASSED >>\n", 'green') assert True
def test_main(): units = 6 batch_shape = (8, 100, 2 * units) iterations = 20 kwargs1 = dict(batch_shape=batch_shape, units=units, bidirectional=False, IMPORTS=IMPORTS) kwargs2 = dict(batch_shape=batch_shape, units=units, bidirectional=True, IMPORTS=IMPORTS) if TF_2 and not TF_KERAS: rnn_layers = GRU, SimpleRNN else: rnn_layers = LSTM, GRU, SimpleRNN if USING_GPU: rnn_layers += CuDNNLSTM, CuDNNGRU model_names = [layer.__name__ for layer in rnn_layers] model_names = [(prefix + name) for prefix in ("uni-", "bi-") for name in model_names] configs = [ dict(rnn_layer=rnn_layer, **kwargs) for kwargs in (kwargs1, kwargs2) for rnn_layer in rnn_layers ] tests_ran = 0 for config, model_name in zip(configs, model_names): reset_seeds(reset_graph_with_backend=K) model = make_model(**config) train_model(model, iterations) rnn_summary(model.layers[1]) _test_outputs(model) _test_outputs_gradients(model) _test_weights_gradients(model) _test_weights(model) tests_ran += 1 cprint("\n<< %s TESTED >>\n" % model_name, 'green') assert tests_ran == len(configs) cprint("\n<< ALL MODELS TESTED >>\n", 'green')
def test_envs(): # pseudo-tests for coverage for different env flags reset_seeds(reset_graph_with_backend=K) units = 6 batch_shape = (8, 100, 2 * units) x, y, sw = make_data(batch_shape, units) from importlib import reload from see_rnn import inspect_gen, inspect_rnn, utils, _backend for flag in ['1', '0']: os.environ["TF_KERAS"] = flag TF_KERAS = os.environ["TF_KERAS"] == '1' reload(_backend) reload(utils) reload(inspect_gen) reload(inspect_rnn) from see_rnn.inspect_gen import get_gradients as glg from see_rnn.inspect_rnn import rnn_summary as rs from see_rnn.utils import _validate_rnn_type as _vrt reset_seeds(reset_graph_with_backend=K) if TF_KERAS: from tensorflow.keras.layers import Input, Bidirectional from tensorflow.keras.layers import GRU as _GRU from tensorflow.keras.models import Model import tensorflow.keras.backend as _K else: from keras.layers import Input, Bidirectional from keras.layers import GRU as _GRU from keras.models import Model import keras.backend as _K reset_seeds(reset_graph_with_backend=_K) new_imports = dict(Input=Input, Bidirectional=Bidirectional, Model=Model) model = make_model(_GRU, batch_shape, new_imports=new_imports, IMPORTS=IMPORTS) pass_on_error(model, x, y, 1) # possibly _backend-induced err pass_on_error(glg, model, 1, x, y) rs(model.layers[1]) from see_rnn.inspect_rnn import get_rnn_weights as grw grw(model, 1, concat_gates=False, as_tensors=True) grw(model, 1, concat_gates=False, as_tensors=False) _test_outputs(model) setattr(model.layers[2].cell, 'get_weights', None) get_rnn_weights(model, 2, concat_gates=True, as_tensors=False) _model = _make_nonrnn_model() pass_on_error(_vrt, _model.layers[1]) del model, _model assert True cprint("\n<< ENV TESTS PASSED >>\n", 'green')
def test_misc(): # test miscellaneous functionalities units = 6 batch_shape = (8, 100, 2 * units) reset_seeds(reset_graph_with_backend=K) model = make_model(GRU, batch_shape, activation='relu', recurrent_dropout=0.3, IMPORTS=IMPORTS) x, y, sw = make_data(batch_shape, units) model.train_on_batch(x, y, sw) weights_norm(model, 'gru', omit_names='bias', verbose=1) weights_norm(model, ['gru', 1, (1, 1)], norm_fn=np.abs) stats = weights_norm(model, 'gru') weights_norm(model, 'gru', _dict=stats) grads = get_gradients(model, 1, x, y) get_gradients(model, 1, x, y, as_dict=True) get_gradients(model, ['gru', 1], x, y) get_outputs(model, ['gru', 1], x) features_1D(grads, subplot_samples=True, tight=True, borderwidth=2, share_xy=False) with tempdir() as dirpath: features_0D(grads[0], savepath=os.path.join(dirpath, 'img.png')) with tempdir() as dirpath: features_1D(grads[0], subplot_samples=True, annotations=[1, 'pi'], savepath=os.path.join(dirpath, 'img.png')) features_2D(grads.T, n_rows=1.5, tight=True, borderwidth=2) with tempdir() as dirpath: features_2D(grads.T[:, :, 0], norm='auto', savepath=os.path.join(dirpath, 'img.png')) with tempdir() as dirpath: features_hist(grads, show_borders=False, borderwidth=1, annotations=[0], show_xy_ticks=[0, 0], share_xy=(1, 1), title="grads", savepath=os.path.join(dirpath, 'img.png')) with tempdir() as dirpath: features_hist_v2(list(grads[:, :4, :3]), colnames=list('abcd'), show_borders=False, xlims=(-.01, .01), ylim=100, borderwidth=1, show_xy_ticks=[0, 0], side_annot='row', share_xy=True, title="Grads", savepath=os.path.join(dirpath, 'img.png')) features_hist(grads, center_zero=True, xlims=(-1, 1), share_xy=0) features_hist_v2(list(grads[:, :4, :3]), center_zero=True, xlims=(-1, 1), share_xy=(False, False)) with tempdir() as dirpath: rnn_histogram(model, 1, show_xy_ticks=[0, 0], equate_axes=2, savepath=os.path.join(dirpath, 'img.png')) rnn_histogram(model, 1, equate_axes=False, configs={ 'tight': dict(left=0, right=1), 'plot': dict(color='red'), 'title': dict(fontsize=14), }) rnn_heatmap(model, 1, cmap=None, normalize=True, show_borders=False) rnn_heatmap(model, 1, cmap=None, norm='auto', absolute_value=True) rnn_heatmap(model, 1, norm=None) with tempdir() as dirpath: rnn_heatmap(model, 1, norm=(-.004, .004), savepath=os.path.join(dirpath, 'img.png')) hist_clipped(grads, peaks_to_clip=2) _, ax = plt.subplots(1, 1) hist_clipped(grads, peaks_to_clip=2, ax=ax, annot_kw=dict(fontsize=15)) get_full_name(model, 'gru') get_full_name(model, 1) pass_on_error(get_full_name, model, 'croc') get_weights(model, 'gru', as_dict=False) get_weights(model, 'gru', as_dict=True) get_weights(model, 'gru/bias') get_weights(model, ['gru', 1, (1, 1)]) pass_on_error(get_weights, model, 'gru/goo') get_weights(model, '*') get_gradients(model, '*', x, y) get_outputs(model, '*', x) from see_rnn.utils import _filter_duplicates_by_keys keys, data = _filter_duplicates_by_keys(list('abbc'), [1, 2, 3, 4]) assert keys == ['a', 'b', 'c'] assert data == [1, 2, 4] keys, data = _filter_duplicates_by_keys(list('abbc'), [1, 2, 3, 4], [5, 6, 7, 8]) assert keys == ['a', 'b', 'c'] assert data[0] == [1, 2, 4] and data[1] == [5, 6, 8] from see_rnn.inspect_gen import get_layer, detect_nans get_layer(model, 'gru') get_rnn_weights(model, 1, concat_gates=False, as_tensors=True) rnn_heatmap(model, 1, input_data=x, labels=y, mode='weights') _test_prefetched_data(model) # test NaN/Inf detection nan_txt = detect_nans(np.array([1] * 9999 + [np.nan])).replace('\n', ' ') print(nan_txt) # case: print as quantity data = np.array([np.nan, np.inf, -np.inf, 0]) print(detect_nans(data, include_inf=True)) print(detect_nans(data, include_inf=False)) data = np.array([np.inf, 0]) print(detect_nans(data, include_inf=True)) detect_nans(np.array([0])) K.set_value(model.optimizer.lr, 1e12) train_model(model, iterations=10) rnn_histogram(model, 1) rnn_heatmap(model, 1) del model reset_seeds(reset_graph_with_backend=K) # test SimpleRNN & other _model = make_model(SimpleRNN, batch_shape, units=128, use_bias=False, IMPORTS=IMPORTS) train_model(_model, iterations=1) # TF2-Keras-Graph bug workaround rnn_histogram(_model, 1) # test _pretty_hist K.set_value(_model.optimizer.lr, 1e50) # force NaNs train_model(_model, iterations=20) rnn_heatmap(_model, 1) data = get_rnn_weights(_model, 1) rnn_heatmap(_model, 1, input_data=x, labels=y, data=data) os.environ["TF_KERAS"] = '0' get_rnn_weights(_model, 1, concat_gates=False) del _model assert True cprint("\n<< MISC TESTS PASSED >>\n", 'green')