def test_history_per_batch_plot(outdir):
hist = HistoryPerBatch()
hist.params = {}
hist.params['metrics'] = ['loss', 'val_loss']
hist.on_train_begin(0)
path_cb = str(outdir.join("callback_plot.png"))
plot_cb = hist.plot_callback(fname=path_cb)
n = 50
mean = 1/np.arange(1, n+1)
std = 1/np.arange(1, n+1)
for e in range(n):
hist.on_epoch_begin(e)
for b in range(100):
hist.on_batch_begin(b)
hist.on_batch_end(b, logs={'loss': float(np.random.normal(mean[e], std[e], 1))})
hist.on_epoch_end(e, logs={'val_loss': float(np.random.normal(mean[e], std[e], 1))})
plot_cb.on_epoch_end(e)
fig, axes = hist.plot()
path1 = str(outdir.join("callback_history.png"))
fig.savefig(path1)
path2 = str(outdir.join("callback_history2.png"))
hist.plot(save_as=path2)
filecmp.cmp(path1, path2)
filecmp.cmp(path_cb, path2)
def test_history_per_batch(tmpdir):
hist = HistoryPerBatch(str(tmpdir))
hist.params = {}
hist.params['metrics'] = ['loss', 'val_loss']
hist.on_epoch_begin(0)
losses = [[]]
for i in range(5):
loss = float(np.random.sample(1))
hist.on_batch_end(i, logs={'loss': loss})
losses[-1].append(loss)
hist.on_epoch_end(0, logs={'loss': 1, 'val_loss': 2})
losses.append([])
hist.on_epoch_begin(1)
for i in range(5):
loss = float(np.random.sample(1))
hist.on_batch_end(i, logs={'loss': loss})
losses[-1].append(loss)
hist.on_epoch_end(1, logs={'loss': 1, 'val_loss': 2})
losses.append([])
hist.on_epoch_begin(2)
for i in range(5):
loss = float(np.random.sample(1))
hist.on_batch_end(i, logs={'loss': loss})
losses[-1].append(loss)
hist.on_epoch_end(2, logs={'loss': 1, 'val_loss': 2})
with pytest.warns(DeprecationWarning):
assert hist.history['loss'] == losses
assert hist.epoch_history['loss'] == [1, 1, 1]
assert hist.epoch_history['val_loss'] == [2, 2, 2]
hist.on_train_end()
assert tmpdir.join("history.json").exists()
assert tmpdir.join("history.png").exists()