Python numpy.add() Examples
The following are 30
code examples of numpy.add().
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Example #1
| Source File: sgan.py From Keras-GAN with MIT License | 8 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(1, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
Example #2
| Source File: context_encoder.py From Keras-GAN with MIT License | 7 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.missing_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, kernel_size=3, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=self.missing_shape)
validity = model(img)
return Model(img, validity)
Example #3
| Source File: cogan.py From Keras-GAN with MIT License | 7 votes |
|
def build_discriminators(self):
img1 = Input(shape=self.img_shape)
img2 = Input(shape=self.img_shape)
# Shared discriminator layers
model = Sequential()
model.add(Flatten(input_shape=self.img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
img1_embedding = model(img1)
img2_embedding = model(img2)
# Discriminator 1
validity1 = Dense(1, activation='sigmoid')(img1_embedding)
# Discriminator 2
validity2 = Dense(1, activation='sigmoid')(img2_embedding)
return Model(img1, validity1), Model(img2, validity2)
Example #4
| Source File: BuildAdjacency.py From sparse-subspace-clustering-python with MIT License | 6 votes |
|
def BuildAdjacency(CMat, K):
CMat = CMat.astype(float)
CKSym = None
N, _ = CMat.shape
CAbs = np.absolute(CMat).astype(float)
for i in range(0, N):
c = CAbs[:, i]
PInd = np.flip(np.argsort(c), 0)
CAbs[:, i] = CAbs[:, i] / float(np.absolute(c[PInd[0]]))
CSym = np.add(CAbs, CAbs.T).astype(float)
if K != 0:
Ind = np.flip(np.argsort(CSym, axis=0), 0)
CK = np.zeros([N, N]).astype(float)
for i in range(0, N):
for j in range(0, K):
CK[Ind[j, i], i] = CSym[Ind[j, i], i] / float(np.absolute(CSym[Ind[0, i], i]))
CKSym = np.add(CK, CK.T)
else:
CKSym = CSym
return CKSym
Example #5
| Source File: bigan.py From Keras-GAN with MIT License | 6 votes |
|
def build_encoder(self):
model = Sequential()
model.add(Flatten(input_shape=self.img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(self.latent_dim))
model.summary()
img = Input(shape=self.img_shape)
z = model(img)
return Model(img, z)
Example #6
| Source File: bgan.py From Keras-GAN with MIT License | 6 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Flatten(input_shape=self.img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=self.img_shape)
validity = model(img)
return Model(img, validity)
Example #7
| Source File: ccgan.py From Keras-GAN with MIT License | 6 votes |
|
def build_discriminator(self):
img = Input(shape=self.img_shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape))
model.add(LeakyReLU(alpha=0.8))
model.add(Conv2D(128, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.add(Conv2D(256, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.summary()
img = Input(shape=self.img_shape)
features = model(img)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features)
label = Flatten()(features)
label = Dense(self.num_classes+1, activation="softmax")(label)
return Model(img, [validity, label])
Example #8
| Source File: bgan.py From Keras-GAN with MIT License | 6 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(256, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod(self.img_shape), activation='tanh'))
model.add(Reshape(self.img_shape))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
Example #9
| Source File: gan.py From Keras-GAN with MIT License | 6 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Flatten(input_shape=self.img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=self.img_shape)
validity = model(img)
return Model(img, validity)
Example #10
| Source File: gan.py From Keras-GAN with MIT License | 6 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(256, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod(self.img_shape), activation='tanh'))
model.add(Reshape(self.img_shape))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
Example #11
| Source File: rsp_findpeaks.py From NeuroKit with MIT License | 6 votes |
|
def _rsp_findpeaks_outliers(rsp_cleaned, extrema, amplitude_min=0.3):
# Only consider those extrema that have a minimum vertical distance to
# their direct neighbor, i.e., define outliers in absolute amplitude
# difference between neighboring extrema.
vertical_diff = np.abs(np.diff(rsp_cleaned[extrema]))
median_diff = np.median(vertical_diff)
min_diff = np.where(vertical_diff > (median_diff * amplitude_min))[0]
extrema = extrema[min_diff]
# Make sure that the alternation of peaks and troughs is unbroken. If
# alternation of sign in extdiffs is broken, remove the extrema that
# cause the breaks.
amplitudes = rsp_cleaned[extrema]
extdiffs = np.sign(np.diff(amplitudes))
extdiffs = np.add(extdiffs[0:-1], extdiffs[1:])
removeext = np.where(extdiffs != 0)[0] + 1
extrema = np.delete(extrema, removeext)
amplitudes = np.delete(amplitudes, removeext)
return extrema, amplitudes
Example #12
| Source File: infogan.py From Keras-GAN with MIT License | 6 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(self.channels, kernel_size=3, padding='same'))
model.add(Activation("tanh"))
gen_input = Input(shape=(self.latent_dim,))
img = model(gen_input)
model.summary()
return Model(gen_input, img)
Example #13
| Source File: wgan.py From Keras-GAN with MIT License | 6 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=4, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=4, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(Conv2D(self.channels, kernel_size=4, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
Example #14
| Source File: test_old_ma.py From recruit with Apache License 2.0 | 6 votes |
|
def test_testAddSumProd(self):
# Test add, sum, product.
(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
assert_(eq(np.add.reduce(x), add.reduce(x)))
assert_(eq(np.add.accumulate(x), add.accumulate(x)))
assert_(eq(4, sum(array(4), axis=0)))
assert_(eq(4, sum(array(4), axis=0)))
assert_(eq(np.sum(x, axis=0), sum(x, axis=0)))
assert_(eq(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0)))
assert_(eq(np.sum(x, 0), sum(x, 0)))
assert_(eq(np.product(x, axis=0), product(x, axis=0)))
assert_(eq(np.product(x, 0), product(x, 0)))
assert_(eq(np.product(filled(xm, 1), axis=0),
product(xm, axis=0)))
if len(s) > 1:
assert_(eq(np.concatenate((x, y), 1),
concatenate((xm, ym), 1)))
assert_(eq(np.add.reduce(x, 1), add.reduce(x, 1)))
assert_(eq(np.sum(x, 1), sum(x, 1)))
assert_(eq(np.product(x, 1), product(x, 1)))
Example #15
| Source File: lsgan.py From Keras-GAN with MIT License | 6 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(256, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod(self.img_shape), activation='tanh'))
model.add(Reshape(self.img_shape))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
Example #16
| Source File: test_core.py From recruit with Apache License 2.0 | 6 votes |
|
def test_addsumprod(self):
# Tests add, sum, product.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
assert_equal(np.add.reduce(x), add.reduce(x))
assert_equal(np.add.accumulate(x), add.accumulate(x))
assert_equal(4, sum(array(4), axis=0))
assert_equal(4, sum(array(4), axis=0))
assert_equal(np.sum(x, axis=0), sum(x, axis=0))
assert_equal(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0))
assert_equal(np.sum(x, 0), sum(x, 0))
assert_equal(np.product(x, axis=0), product(x, axis=0))
assert_equal(np.product(x, 0), product(x, 0))
assert_equal(np.product(filled(xm, 1), axis=0), product(xm, axis=0))
s = (3, 4)
x.shape = y.shape = xm.shape = ym.shape = s
if len(s) > 1:
assert_equal(np.concatenate((x, y), 1), concatenate((xm, ym), 1))
assert_equal(np.add.reduce(x, 1), add.reduce(x, 1))
assert_equal(np.sum(x, 1), sum(x, 1))
assert_equal(np.product(x, 1), product(x, 1))
Example #17
| Source File: test_feed.py From tensortrade with Apache License 2.0 | 6 votes |
|
def test_multi_step_adding():
a1 = Stream([1, 2, 3]).rename("a1")
a2 = Stream([4, 5, 6]).rename("a2")
t1 = BinOp(np.add)(a1, a2).rename("t1")
t2 = BinOp(np.add)(t1, a2).rename("t2")
feed = DataFeed([a1, a2, t1, t2])
output = feed.next()
assert output == {'a1': 1, 'a2': 4, 't1': 5, 't2': 9}
feed = DataFeed([a1, a2, t2])
output = feed.next()
assert output == {'a1': 1, 'a2': 4, 't2': 9}
Example #18
| Source File: dualgan.py From Keras-GAN with MIT License | 6 votes |
|
def build_generator(self):
X = Input(shape=(self.img_dim,))
model = Sequential()
model.add(Dense(256, input_dim=self.img_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dropout(0.4))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dropout(0.4))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dropout(0.4))
model.add(Dense(self.img_dim, activation='tanh'))
X_translated = model(X)
return Model(X, X_translated)
Example #19
| Source File: lsgan.py From Keras-GAN with MIT License | 6 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Flatten(input_shape=self.img_shape))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
# (!!!) No softmax
model.add(Dense(1))
model.summary()
img = Input(shape=self.img_shape)
validity = model(img)
return Model(img, validity)
Example #20
| Source File: heston.py From tensortrade with Apache License 2.0 | 6 votes |
|
def geometric_brownian_motion_jump_diffusion_log_returns(params: ModelParameters):
"""
Constructs combines a geometric brownian motion process
(log returns) with a jump diffusion process (log returns) to produce
a sequence of gbm jump returns.
Arguments:
params : ModelParameters
The parameters for the stochastic model.
Returns:
A GBM process with jumps in it
"""
jump_diffusion = jump_diffusion_process(params)
geometric_brownian_motion = geometric_brownian_motion_log_returns(params)
return np.add(jump_diffusion, geometric_brownian_motion)
Example #21
| Source File: test_core.py From recruit with Apache License 2.0 | 6 votes |
|
def test_datafriendly_add(self):
# Test keeping data w/ (inplace) addition
x = array([1, 2, 3], mask=[0, 0, 1])
# Test add w/ scalar
xx = x + 1
assert_equal(xx.data, [2, 3, 3])
assert_equal(xx.mask, [0, 0, 1])
# Test iadd w/ scalar
x += 1
assert_equal(x.data, [2, 3, 3])
assert_equal(x.mask, [0, 0, 1])
# Test add w/ array
x = array([1, 2, 3], mask=[0, 0, 1])
xx = x + array([1, 2, 3], mask=[1, 0, 0])
assert_equal(xx.data, [1, 4, 3])
assert_equal(xx.mask, [1, 0, 1])
# Test iadd w/ array
x = array([1, 2, 3], mask=[0, 0, 1])
x += array([1, 2, 3], mask=[1, 0, 0])
assert_equal(x.data, [1, 4, 3])
assert_equal(x.mask, [1, 0, 1])
Example #22
| Source File: aae.py From Keras-GAN with MIT License | 5 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Dense(512, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation="sigmoid"))
model.summary()
encoded_repr = Input(shape=(self.latent_dim, ))
validity = model(encoded_repr)
return Model(encoded_repr, validity)
Example #23
| Source File: test_core.py From recruit with Apache License 2.0 | 5 votes |
|
def test_basic_arithmetic(self):
# Test of basic arithmetic.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
a2d = array([[1, 2], [0, 4]])
a2dm = masked_array(a2d, [[0, 0], [1, 0]])
assert_equal(a2d * a2d, a2d * a2dm)
assert_equal(a2d + a2d, a2d + a2dm)
assert_equal(a2d - a2d, a2d - a2dm)
for s in [(12,), (4, 3), (2, 6)]:
x = x.reshape(s)
y = y.reshape(s)
xm = xm.reshape(s)
ym = ym.reshape(s)
xf = xf.reshape(s)
assert_equal(-x, -xm)
assert_equal(x + y, xm + ym)
assert_equal(x - y, xm - ym)
assert_equal(x * y, xm * ym)
assert_equal(x / y, xm / ym)
assert_equal(a10 + y, a10 + ym)
assert_equal(a10 - y, a10 - ym)
assert_equal(a10 * y, a10 * ym)
assert_equal(a10 / y, a10 / ym)
assert_equal(x + a10, xm + a10)
assert_equal(x - a10, xm - a10)
assert_equal(x * a10, xm * a10)
assert_equal(x / a10, xm / a10)
assert_equal(x ** 2, xm ** 2)
assert_equal(abs(x) ** 2.5, abs(xm) ** 2.5)
assert_equal(x ** y, xm ** ym)
assert_equal(np.add(x, y), add(xm, ym))
assert_equal(np.subtract(x, y), subtract(xm, ym))
assert_equal(np.multiply(x, y), multiply(xm, ym))
assert_equal(np.divide(x, y), divide(xm, ym))
Example #24
| Source File: test_old_ma.py From recruit with Apache License 2.0 | 5 votes |
|
def test_testUfuncRegression(self):
f_invalid_ignore = [
'sqrt', 'arctanh', 'arcsin', 'arccos',
'arccosh', 'arctanh', 'log', 'log10', 'divide',
'true_divide', 'floor_divide', 'remainder', 'fmod']
for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
'sin', 'cos', 'tan',
'arcsin', 'arccos', 'arctan',
'sinh', 'cosh', 'tanh',
'arcsinh',
'arccosh',
'arctanh',
'absolute', 'fabs', 'negative',
'floor', 'ceil',
'logical_not',
'add', 'subtract', 'multiply',
'divide', 'true_divide', 'floor_divide',
'remainder', 'fmod', 'hypot', 'arctan2',
'equal', 'not_equal', 'less_equal', 'greater_equal',
'less', 'greater',
'logical_and', 'logical_or', 'logical_xor']:
try:
uf = getattr(umath, f)
except AttributeError:
uf = getattr(fromnumeric, f)
mf = getattr(np.ma, f)
args = self.d[:uf.nin]
with np.errstate():
if f in f_invalid_ignore:
np.seterr(invalid='ignore')
if f in ['arctanh', 'log', 'log10']:
np.seterr(divide='ignore')
ur = uf(*args)
mr = mf(*args)
assert_(eq(ur.filled(0), mr.filled(0), f))
assert_(eqmask(ur.mask, mr.mask))
Example #25
| Source File: test_mixins.py From recruit with Apache License 2.0 | 5 votes |
|
def __init__(self, value):
self.value = np.asarray(value)
# One might also consider adding the built-in list type to this
# list, to support operations like np.add(array_like, list)
Example #26
| Source File: acgan.py From Keras-GAN with MIT License | 5 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten())
model.summary()
img = Input(shape=self.img_shape)
# Extract feature representation
features = model(img)
# Determine validity and label of the image
validity = Dense(1, activation="sigmoid")(features)
label = Dense(self.num_classes, activation="softmax")(features)
return Model(img, [validity, label])
Example #27
| Source File: test_core.py From recruit with Apache License 2.0 | 5 votes |
|
def _do_add_test(self, add):
# sanity check
assert_(add(np.ma.masked, 1) is np.ma.masked)
# now try with a vector
vector = np.array([1, 2, 3])
result = add(np.ma.masked, vector)
# lots of things could go wrong here
assert_(result is not np.ma.masked)
assert_(not isinstance(result, np.ma.core.MaskedConstant))
assert_equal(result.shape, vector.shape)
assert_equal(np.ma.getmask(result), np.ones(vector.shape, dtype=bool))
Example #28
| Source File: cogan.py From Keras-GAN with MIT License | 5 votes |
|
def build_generators(self):
# Shared weights between generators
model = Sequential()
model.add(Dense(256, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
noise = Input(shape=(self.latent_dim,))
feature_repr = model(noise)
# Generator 1
g1 = Dense(1024)(feature_repr)
g1 = LeakyReLU(alpha=0.2)(g1)
g1 = BatchNormalization(momentum=0.8)(g1)
g1 = Dense(np.prod(self.img_shape), activation='tanh')(g1)
img1 = Reshape(self.img_shape)(g1)
# Generator 2
g2 = Dense(1024)(feature_repr)
g2 = LeakyReLU(alpha=0.2)(g2)
g2 = BatchNormalization(momentum=0.8)(g2)
g2 = Dense(np.prod(self.img_shape), activation='tanh')(g2)
img2 = Reshape(self.img_shape)(g2)
model.summary()
return Model(noise, img1), Model(noise, img2)
Example #29
| Source File: dcgan.py From Keras-GAN with MIT License | 5 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=self.img_shape)
validity = model(img)
return Model(img, validity)
Example #30
| Source File: dualgan.py From Keras-GAN with MIT License | 5 votes |
|
def build_discriminator(self):
img = Input(shape=(self.img_dim,))
model = Sequential()
model.add(Dense(512, input_dim=self.img_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1))
validity = model(img)
return Model(img, validity)
