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Fix test; activate all virtual tests
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@dsblank
dsblank committed Sep 8, 2018
1 parent 9db37c8 commit 1e6942d
Showing 1 changed file with 108 additions and 132 deletions.
240 changes: 108 additions & 132 deletions conx/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1838,10 +1838,7 @@ class VirtualDataset(Dataset):
>>> import conx as cx
>>> import numpy as np
>>> import keras
>>> import random
>>> from distutils.version import LooseVersion
>>> def f(self, cache):
... pos1 = cache * self._cache_size
Expand All @@ -1855,14 +1852,14 @@ class VirtualDataset(Dataset):
>>> ds.split(.25)
>>> ds.inputs[0], ds.inputs[-1]
([0, 0], [999, 999])
>>> ds.inputs[0][0] == 0.0, ds.inputs[-1][0] == 999
True, True
>>> ds.train_inputs[0], ds.train_inputs[-1]
([0, 0], [749, 749])
>>> ds.train_inputs[0][0] == 0, ds.train_inputs[-1][0] == 749
True, True
>>> ds.test_inputs[0], ds.test_inputs[-1]
([750, 750], [999, 999])
>>> ds.test_inputs[0][0] == 750, ds.test_inputs[-1][0] == 999
True, True
>>> def f(self, pos):
... return [[pos], [pos]], [[pos]]
Expand All @@ -1872,14 +1869,14 @@ class VirtualDataset(Dataset):
>>> ds.split(.25)
>>> ds.inputs[0], ds.inputs[-1]
([0, 0], [999, 999])
>>> ds.inputs[0][0] == 0.0, ds.inputs[-1][0] == 999
True, True
>>> ds.train_inputs[0], ds.train_inputs[-1]
([0, 0], [749, 749])
>>> ds.train_inputs[0][0] == 0, ds.train_inputs[-1][0] == 749
True, True
>>> ds.test_inputs[0], ds.test_inputs[-1]
([750, 750], [999, 999])
>>> ds.test_inputs[0][0] == 750, ds.test_inputs[-1][0] == 999
True, True
>>> def test_dataset(net):
... net.dataset.split(.1)
Expand All @@ -1900,148 +1897,127 @@ class VirtualDataset(Dataset):
generator_ordered = False, load_cache_direct = False, with function(position):
>>> if LooseVersion(keras.__version__) >= LooseVersion("2.2.0"):
... def f(self, pos):
... return ([pos/100, pos/100], [pos/100])
... dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... load_cache_direct=False,
... cache_size=16)
... net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
... net.compile(error="mse", optimizer="adam")
... net.set_dataset(dataset)
... test_dataset(net) # doctest: +ELLIPSIS
... else:
... print("Evaluating initial ...")
>>> def f(self, pos):
... return ([pos/100, pos/100], [pos/100])
>>> dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... load_cache_direct=False,
... cache_size=16)
>>> net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
>>> net.compile(error="mse", optimizer="adam")
>>> net.set_dataset(dataset)
>>> test_dataset(net) # doctest: +ELLIPSIS
Evaluating initial ...
generator_ordered = False, load_cache_direct = True, with function(cache):
>>> if LooseVersion(keras.__version__) >= LooseVersion("2.2.0"):
... def f(self, cache):
... i = cache * 16
... while True:
... all_inputs = [[]]
... all_targets = [[]]
... while i < (cache + 1) * 16:
... all_inputs[0].append([i/100, i/100])
... all_targets[0].append([i/100])
... i += 1
... return ([np.array(inputs) for inputs in all_inputs],
... [np.array(targets) for targets in all_targets])
... dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... cache_size=16)
... net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
... net.compile(error="mse", optimizer="adam")
... net.set_dataset(dataset)
... test_dataset(net) # doctest: +ELLIPSIS
... else:
... print("Evaluating initial ...")
>>> def f(self, cache):
... i = cache * 16
... while True:
... all_inputs = [[]]
... all_targets = [[]]
... while i < (cache + 1) * 16:
... all_inputs[0].append([i/100, i/100])
... all_targets[0].append([i/100])
... i += 1
... return ([np.array(inputs) for inputs in all_inputs],
... [np.array(targets) for targets in all_targets])
>>> dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... cache_size=16)
>>> net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
>>> net.compile(error="mse", optimizer="adam")
>>> net.set_dataset(dataset)
>>> test_dataset(net) # doctest: +ELLIPSIS
Evaluating initial ...
generator_ordered = True, load_cache_direct = False, with generator function():
>>> if LooseVersion(keras.__version__) >= LooseVersion("2.2.0"):
... def f(self):
... i = 0
... while True:
... yield ([i/100, i/100], [i/100])
... i += 1
... dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... generator_ordered=True,
... load_cache_direct=False,
... cache_size=16)
... net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
... net.compile(error="mse", optimizer="adam")
... net.set_dataset(dataset)
... test_dataset(net) # doctest: +ELLIPSIS
... else:
... print("Evaluating initial ...")
>>> def f(self):
... i = 0
... while True:
... yield ([i/100, i/100], [i/100])
... i += 1
>>> dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... generator_ordered=True,
... load_cache_direct=False,
... cache_size=16)
>>> net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
>>> net.compile(error="mse", optimizer="adam")
>>> net.set_dataset(dataset)
>>> test_dataset(net) # doctest: +ELLIPSIS
Evaluating initial ...
generator_ordered = True, load_cache_direct = True, with generator function():
>>> if LooseVersion(keras.__version__) >= LooseVersion("2.2.0"):
... def f(self):
... i = 0
... while True:
... i_16 = i + 16
... all_inputs = [[]]
... all_targets = [[]]
... while i < i_16:
... all_inputs[0].append([i/100, i/100])
... all_targets[0].append([i/100])
... i += 1
... yield ([np.array(inputs) for inputs in all_inputs],
... [np.array(targets) for targets in all_targets])
... dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... generator_ordered=True,
... cache_size=16)
... net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
... net.compile(error="mse", optimizer="adam")
... net.set_dataset(dataset)
... test_dataset(net) # doctest: +ELLIPSIS
... else:
... print("Evaluating initial ...")
>>> def f(self):
... i = 0
... while True:
... i_16 = i + 16
... all_inputs = [[]]
... all_targets = [[]]
... while i < i_16:
... all_inputs[0].append([i/100, i/100])
... all_targets[0].append([i/100])
... i += 1
... yield ([np.array(inputs) for inputs in all_inputs],
... [np.array(targets) for targets in all_targets])
>>> dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... generator_ordered=True,
... cache_size=16)
>>> net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
>>> net.compile(error="mse", optimizer="adam")
>>> net.set_dataset(dataset)
>>> test_dataset(net) # doctest: +ELLIPSIS
Evaluating initial ...
generator_ordered = True, load_cache_direct = False, with generator function() (showing another
function style):
>>> if LooseVersion(keras.__version__) >= LooseVersion("2.2.0"):
... def f(self):
... for i in range(100):
... yield ([i/100, i/100], [i/100])
... dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... generator_ordered=True,
... load_cache_direct=False,
... cache_size=16)
... net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
... net.compile(error="mse", optimizer="adam")
... net.set_dataset(dataset)
... test_dataset(net) # doctest: +ELLIPSIS
... else:
... print("Evaluating initial ...")
>>> def f(self):
... for i in range(100):
... yield ([i/100, i/100], [i/100])
>>> dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... generator_ordered=True,
... load_cache_direct=False,
... cache_size=16)
>>> net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
>>> net.compile(error="mse", optimizer="adam")
>>> net.set_dataset(dataset)
>>> test_dataset(net) # doctest: +ELLIPSIS
Evaluating initial ...
generator_ordered = False, load_cache_direct = False, with generator function():
>>> if LooseVersion(keras.__version__) >= LooseVersion("2.2.0"):
... def f(self):
... while True:
... r = random.random()
... yield ([r, r], [r])
... dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... load_cache_direct=False,
... cache_size=16)
... net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
... net.compile(error="mse", optimizer="adam")
... net.set_dataset(dataset)
... test_dataset(net) # doctest: +ELLIPSIS
... else:
... print("Evaluating initial ...")
>>> def f(self):
... while True:
... r = random.random()
... yield ([r, r], [r])
>>> dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... load_cache_direct=False,
... cache_size=16)
>>> net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
>>> net.compile(error="mse", optimizer="adam")
>>> net.set_dataset(dataset)
>>> test_dataset(net) # doctest: +ELLIPSIS
Evaluating initial ...
generator_ordered = False, load_cache_direct = True, with generator function():
>>> if LooseVersion(keras.__version__) >= LooseVersion("2.2.0"):
... def f(self):
... while True:
... all_inputs = [[]]
... all_targets = [[]]
... for i in range(16):
... r = random.random()
... all_inputs[0].append([r, r])
... all_targets[0].append([r])
... yield ([np.array(inputs) for inputs in all_inputs],
... [np.array(targets) for targets in all_targets])
... dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... cache_size=16)
... net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
... net.compile(error="mse", optimizer="adam")
... net.set_dataset(dataset)
... test_dataset(net) # doctest: +ELLIPSIS
... else:
... print("Evaluating initial ...")
>>> def f(self):
... while True:
... all_inputs = [[]]
... all_targets = [[]]
... for i in range(16):
... r = random.random()
... all_inputs[0].append([r, r])
... all_targets[0].append([r])
... yield ([np.array(inputs) for inputs in all_inputs],
... [np.array(targets) for targets in all_targets])
>>> dataset = cx.VirtualDataset(f, 100, [(2,)], [(1,)], [(0,1)], [(0,1)],
... cache_size=16)
>>> net = cx.Network("GEN", 2, 3, 1, activation="sigmoid")
>>> net.compile(error="mse", optimizer="adam")
>>> net.set_dataset(dataset)
>>> test_dataset(net) # doctest: +ELLIPSIS
Evaluating initial ...
"""
Vector = VirtualDataVector
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