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-rw-r--r--tensorflow/python/keras/engine/training_eager_test.py278
1 files changed, 0 insertions, 278 deletions
diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py
index bdb3035129..b0f57f0770 100644
--- a/tensorflow/python/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/engine/training_eager_test.py
@@ -31,284 +31,6 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer
class TrainingTest(test.TestCase):
- def test_fit_on_arrays(self):
- a = keras.layers.Input(shape=(3,), name='input_a')
- b = keras.layers.Input(shape=(3,), name='input_b')
-
- dense = keras.layers.Dense(4, name='dense')
- c = dense(a)
- d = dense(b)
- e = keras.layers.Dropout(0.5, name='dropout')(c)
-
- model = keras.models.Model([a, b], [d, e])
-
- optimizer = RMSPropOptimizer(learning_rate=0.001)
- loss = 'mse'
- loss_weights = [1., 0.5]
- metrics = ['mae']
- model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights)
-
- input_a_np = np.random.random((10, 3))
- input_b_np = np.random.random((10, 3))
-
- output_d_np = np.random.random((10, 4))
- output_e_np = np.random.random((10, 4))
-
- # Test fit at different verbosity
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- epochs=1,
- batch_size=5,
- verbose=0)
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- epochs=1,
- batch_size=5,
- verbose=1)
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- epochs=2,
- batch_size=5,
- verbose=2)
-
- # Test with validation data
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- validation_data=([input_a_np, input_b_np], [output_d_np,
- output_e_np]),
- epochs=1,
- batch_size=5,
- verbose=0)
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- validation_data=([input_a_np, input_b_np], [output_d_np,
- output_e_np]),
- epochs=2,
- batch_size=5,
- verbose=1)
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- validation_data=([input_a_np, input_b_np], [output_d_np,
- output_e_np]),
- epochs=2,
- batch_size=5,
- verbose=2)
- model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np])
-
- # Test with validation split
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- epochs=2,
- batch_size=5,
- verbose=0,
- validation_split=0.2)
-
- # Test with dictionary inputs
- model.fit(
- {
- 'input_a': input_a_np,
- 'input_b': input_b_np
- }, {'dense': output_d_np,
- 'dropout': output_e_np},
- epochs=1,
- batch_size=5,
- verbose=0)
- model.fit(
- {
- 'input_a': input_a_np,
- 'input_b': input_b_np
- }, {'dense': output_d_np,
- 'dropout': output_e_np},
- epochs=1,
- batch_size=5,
- verbose=1)
- model.fit(
- {
- 'input_a': input_a_np,
- 'input_b': input_b_np
- }, {'dense': output_d_np,
- 'dropout': output_e_np},
- validation_data=({'input_a': input_a_np,
- 'input_b': input_b_np
- },
- {
- 'dense': output_d_np,
- 'dropout': output_e_np
- }),
- epochs=1,
- batch_size=5,
- verbose=0)
- model.train_on_batch({
- 'input_a': input_a_np,
- 'input_b': input_b_np
- }, {'dense': output_d_np,
- 'dropout': output_e_np})
- # Test with lists for loss, metrics
- loss = ['mae', 'mse']
- metrics = ['acc', 'mae']
- model.compile(optimizer, loss, metrics=metrics)
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- epochs=1,
- batch_size=5,
- verbose=0)
-
- # Test with dictionaries for loss, metrics, loss weights
- loss = {'dense': 'mse', 'dropout': 'mae'}
- loss_weights = {'dense': 1., 'dropout': 0.5}
- metrics = {'dense': 'mse', 'dropout': 'mae'}
- model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights)
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- epochs=1,
- batch_size=5,
- verbose=0)
-
- # Invalid use cases
- with self.assertRaises(AttributeError):
- model.fit(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- epochs=1,
- validation_data=([input_a_np, input_b_np], 0, 0),
- verbose=0)
- with self.assertRaises(ValueError):
- model.train_on_batch({'input_a': input_a_np},
- [output_d_np, output_e_np])
- with self.assertRaises(ValueError):
- model.train_on_batch([input_a_np], [output_d_np, output_e_np])
- with self.assertRaises(AttributeError):
- model.train_on_batch(1, [output_d_np, output_e_np])
- with self.assertRaises(ValueError):
- model.train_on_batch(input_a_np, [output_d_np, output_e_np])
- with self.assertRaises(ValueError):
- bad_input = np.random.random((11, 3))
- model.train_on_batch([bad_input, input_b_np],
- [output_d_np, output_e_np])
- with self.assertRaises(ValueError):
- bad_target = np.random.random((11, 4))
- model.train_on_batch([input_a_np, input_b_np],
- [bad_target, output_e_np])
-
- # Build single-input model
- x = keras.layers.Input(shape=(3,), name='input_a')
- y = keras.layers.Dense(4)(x)
- model = keras.models.Model(x, y)
- model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
- # This will work
- model.fit([input_a_np], output_d_np, epochs=1)
- with self.assertRaises(ValueError):
- model.fit([input_a_np, input_a_np], output_d_np, epochs=1)
-
- def test_evaluate_predict_on_arrays(self):
- a = keras.layers.Input(shape=(3,), name='input_a')
- b = keras.layers.Input(shape=(3,), name='input_b')
-
- dense = keras.layers.Dense(4, name='dense')
- c = dense(a)
- d = dense(b)
- e = keras.layers.Dropout(0.5, name='dropout')(c)
-
- model = keras.models.Model([a, b], [d, e])
-
- optimizer = RMSPropOptimizer(learning_rate=0.001)
- loss = 'mse'
- loss_weights = [1., 0.5]
- metrics = ['acc', 'mae']
- model.compile(
- optimizer,
- loss,
- metrics=metrics,
- loss_weights=loss_weights,
- sample_weight_mode=None)
-
- input_a_np = np.random.random((10, 3))
- input_b_np = np.random.random((10, 3))
-
- output_d_np = np.random.random((10, 4))
- output_e_np = np.random.random((10, 4))
-
- # Test evaluate at different verbosity
- out = model.evaluate(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- batch_size=5,
- verbose=0)
- self.assertEqual(len(out), 7)
- out = model.evaluate(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- batch_size=5,
- verbose=1)
- self.assertEqual(len(out), 7)
- out = model.evaluate(
- [input_a_np, input_b_np], [output_d_np, output_e_np],
- batch_size=5,
- verbose=2)
- self.assertEqual(len(out), 7)
- out = model.test_on_batch([input_a_np, input_b_np],
- [output_d_np, output_e_np])
- self.assertEqual(len(out), 7)
-
- # Test evaluate with dictionary inputs
- model.evaluate(
- {
- 'input_a': input_a_np,
- 'input_b': input_b_np
- }, {'dense': output_d_np,
- 'dropout': output_e_np},
- batch_size=5,
- verbose=0)
- model.evaluate(
- {
- 'input_a': input_a_np,
- 'input_b': input_b_np
- }, {'dense': output_d_np,
- 'dropout': output_e_np},
- batch_size=5,
- verbose=1)
-
- # Test predict
- out = model.predict([input_a_np, input_b_np], batch_size=5)
- self.assertEqual(len(out), 2)
- out = model.predict({'input_a': input_a_np, 'input_b': input_b_np})
- self.assertEqual(len(out), 2)
- out = model.predict_on_batch({
- 'input_a': input_a_np,
- 'input_b': input_b_np
- })
- self.assertEqual(len(out), 2)
-
- def test_invalid_loss_or_metrics(self):
- num_classes = 5
- train_samples = 1000
- test_samples = 1000
- input_dim = 5
-
- model = keras.models.Sequential()
- model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
- model.add(keras.layers.Activation('relu'))
- model.add(keras.layers.Dense(num_classes))
- model.add(keras.layers.Activation('softmax'))
- model.compile(loss='categorical_crossentropy',
- optimizer=RMSPropOptimizer(learning_rate=0.001))
- np.random.seed(1337)
-
- (x_train, y_train), (_, _) = testing_utils.get_test_data(
- train_samples=train_samples,
- test_samples=test_samples,
- input_shape=(input_dim,),
- num_classes=num_classes)
-
- with self.assertRaises(ValueError):
- model.fit(x_train, np.concatenate([y_train, y_train], axis=-1))
-
- with self.assertRaises(TypeError):
- model.compile(loss='categorical_crossentropy',
- optimizer=RMSPropOptimizer(learning_rate=0.001),
- metrics=set(0))
-
- with self.assertRaises(ValueError):
- model.compile(loss=None,
- optimizer='rms')
-
def test_model_methods_with_eager_tensors_multi_io(self):
a = keras.layers.Input(shape=(3,), name='input_a')
b = keras.layers.Input(shape=(3,), name='input_b')
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-29 23:32:02 +0000