def feed_test(inp_layers,
oup_layers,
expected_output,
num_params,
multi_output=False):
inp_layers = to_list(inp_layers)
oup_layers = to_list(oup_layers)
model = Model(inp_layers, oup_layers)
model.compile('sgd', ['mse'] * len(oup_layers))
pred = model.predict([np.array([[1]])] * len(inp_layers))
if not multi_output:
expected_output = [expected_output]
for p, e in zip(pred, expected_output):
assert_allclose(p, e)
# use caller_name to avoid race condition when conducting parallel testing
caller_name = inspect.stack()[1][3]
arch_fname = '/tmp/arch_{}.json'.format(caller_name)
weight_fname = '/tmp/weight_{}.hkl'.format(caller_name)
model.compile('sgd', ['mse'] * len(oup_layers))
if len(model.trainable_params) == 0:
weight_fname = None
model.save_to_file(arch_fname, weight_fname, overwrite=True, indent=2)
model2 = Model.load_from_file(arch_fname, weight_fname)
model2.compile('sgd', ['mse'] * len(oup_layers))
assert len(model.trainable_params) == len(
model2.trainable_params) == num_params
for p1, p2 in zip(model.trainable_params, model2.trainable_params):
assert_allclose(B.eval(p1), B.eval(p1))
for r1, r2 in zip(model.regularizers.values(),
model2.regularizers.values()):
assert str(serialize(r1)) == str(serialize(r2))
for c1, c2 in zip(model.constraints.values(), model2.constraints.values()):
assert str(serialize(c1)) == str(serialize(c2))
def test_fit_evaluate_predict_spec():
x = np.array([[1], [1]])
y = np.array([[1], [1]])
input1 = Input(1, name='input1')
input2 = Input(1, name='input2')
output1 = Dense(1, name='output1')(input1)
output2 = Dense(1, name='output2')(input2)
model0 = Model(input1, output1)
model1 = Model([input1, input2], [output1, output2])
model2 = Model([input1, input2], [output1, output2])
model0.compile('sgd', 'mse', metrics=['acc'])
model1.compile('sgd', loss=['mse', 'mse'], metrics=['acc'])
model2.compile('sgd',
loss={
'output1': 'mse',
'output2': 'mse'
},
metrics={
'output1': 'acc',
'output2': 'acc'
})
model0.predict([1, 1])
model0.evaluate([1, 1], [1, 1])
model0.fit([1, 1], [1, 1], nb_epoch=1)
model1.predict([x, x])
model1.evaluate([x, x], [y, y])
model1.fit([x, x], [y, y], nb_epoch=1)
model2.predict({'input1': x, 'input2': x})
model2.evaluate({'input1': x, 'input2': x}, {'output1': y, 'output2': y})
model2.fit({
'input1': x,
'input2': x
}, {
'output1': y,
'output2': y
},
nb_epoch=1)
def compile_model(inputs, outputs):
model = Model(inputs, outputs)
model.compile('sgd', 'mse')
def layer_test(layer,
inp_vals,
exp_output=None,
random_exp={},
multi_input=False,
debug=False,
input_args={},
test_serialization=True,
train_mode=True):
if multi_input:
input_vals = []
for val in inp_vals:
input_vals.append(np.asarray(val))
else:
input_vals = [np.asarray(inp_vals)]
if exp_output is not None:
exp_output = np.asarray(exp_output)
if 'shape' in input_args:
if 'batch_size' in input_args:
input_shapes = [(input_args['batch_size'], ) + input_args['shape']]
else:
input_shapes = [(None, ) + input_args['shape']]
del input_args['shape']
else:
input_shapes = [val.shape for val in input_vals]
input_layers = [Input(shape[1:], **input_args) for shape in input_shapes]
model = Model(input_layers, layer(unlist_if_one(input_layers)))
model.compile('sgd', 'mse')
output = model.predict(input_vals, train_mode=train_mode)[
0] # result of the first (ant the only) output channel
output_shape = layer.output_shape(unlist_if_one(input_shapes))
# check output(), output_shape() implementation
cls_name = layer.__class__.__name__
if debug:
print(cls_name)
if exp_output is not None:
print("Expected Output:\n{}".format(exp_output))
print("Output:\n{}".format(output))
if exp_output is not None:
print("Expected Output Shape:\n{}".format(exp_output.shape))
print("Output shape:\n{}".format(output_shape))
if debug == 2:
import sys
sys.exit(-1)
if exp_output is not None:
assert_allclose(
output,
exp_output,
err_msg='===={}.output() incorrect!====\n'.format(cls_name))
if None in output_shape:
assert output_shape[0] is None
assert_allclose(
output_shape[1:],
exp_output.shape[1:],
err_msg='===={}.output_shape() incorrect!===='.format(
cls_name))
else:
assert_allclose(
output_shape,
exp_output.shape,
err_msg='===={}.output_shape() incorrect!===='.format(
cls_name))
else:
# No exp_output, test if the shape of the output is the same as that provided by output_shape function.
if None in output_shape:
assert output_shape[0] is None
assert_allclose(
output_shape[1:],
output.shape[1:],
err_msg='===={}.output_shape() incorrect!===='.format(
cls_name))
else:
assert_allclose(
output_shape,
output.shape,
err_msg='===={}.output_shape() incorrect!===='.format(
cls_name))
lim = 1e-2
if 'std' in random_exp:
assert abs(output.std() - random_exp['std']) < lim
if 'mean' in random_exp:
assert abs(output.mean() - random_exp['mean']) < lim
if 'max' in random_exp:
assert abs(output.max() - random_exp['max']) < lim
if 'min' in random_exp:
assert abs(output.min() - random_exp['min']) < lim
if test_serialization:
# check if layer is ok for serialization
arch_fname = '/tmp/arch_{}.json'.format(cls_name)
weight_fname = '/tmp/weight_{}.hkl'.format(cls_name)
if len(model.trainable_params) == 0:
weight_fname = None
model.save_to_file(arch_fname, weight_fname, overwrite=True, indent=2)
try:
model2 = Model.load_from_file(arch_fname, weight_fname)
except:
assert False, '====Reconstruction of the model fails. "{}" serialization problem!===='.format(
cls_name)
model2.compile('sgd', 'mse')
model2_output = model2.predict(input_vals, train_mode=train_mode)[0]
if len(random_exp) == 0:
assert_allclose(
output,
model2_output,
err_msg=
'====Reconstructed model predicts different. "{}" serialization problem!====\n'
.format(cls_name))
row, col, pixel = X_train.shape[1:]
# 4D input.
x = Input(shape=(row, col, pixel))
# Encodes a row of pixels using TimeDistributed Wrapper.
encoded_rows = TimeDistributed(LSTM(output_dim=row_hidden))(x)
# Encodes columns of encoded rows.
encoded_columns = LSTM(col_hidden)(encoded_rows)
# Final predictions and model.
prediction = Dense(nb_classes, activation='softmax')(encoded_columns)
model = Model(inputs=x, outputs=prediction)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# Training.
model.fit(X_train,
y_train,
batch_size=batch_size,
nb_epoch=nb_epochs,
verbose=1,
validation_data=(X_test, y_test))
# Evaluation.
scores = model.evaluate(X_test, y_test)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])