expected string or bytes-like object when calling model subclass - conv-neural-network

Question

I have attempted to write a generative adversarial network. Below is the code of one of the discriminators.
class D1(Layer):
def __init__ (self, input_shape=(256, 256, 3), name='d1', **kwargs):
super(D1, self).__init__(name=name, **kwargs)
self.h1 = Conv2D(64, (3, 3), strides=(1, 1), padding='same')
self.h2 = MaxPooling2D(pool_size=(2, 2), strides=None, padding='same')
self.h3 = LeakyReLU(alpha=0.2)
self.h4 = Conv2D(128, (3, 3), strides=(1, 1), padding='same')
self.h5 = Conv2D(128, (3, 3), strides=(1, 1), padding='same')
self.h6 = MaxPooling2D(pool_size=(2, 2), strides=None, padding='same')
self.h7 = LeakyReLU(alpha=0.2)
self.h8 = Conv2D(256, (3, 3), strides=(1, 1), padding='same')
self.h9 = Conv2D(256, (3, 3), strides=(1, 1), padding='same')
self.h10 = MaxPooling2D(pool_size=(2, 2), strides=None, padding='same')
self.h11 = LeakyReLU(alpha=0.2)
self.h12 = Conv2D(512, (3, 3), strides=(1, 1), padding='same')
self.h13 = Conv2D(512, (3, 3), strides=(1, 1), padding='same')
self.h14 = MaxPooling2D(pool_size=(2, 2), strides=None, padding='same')
self.h15 = Flatten()
self.h16 = Dropout(0.4)
self.D1R = Dense(1, activation='sigmoid')
self.h17 = Dense(4096, activation='relu')
self.h18 = Dense(4096, activation='relu')
self.D1C = Dense(16, activation='sigmoid')
def call(self, inputs):
x = self.h1(inputs)
x = self.h2(x)
x = self.h3(x)
x = self.h4(x)
x = self.h5(x)
x = self.h6(x)
x = self.h7(x)
x = self.h8(x)
x = self.h9(x)
x = self.h10(x)
x = self.h11(x)
x = self.h12(x)
x = self.h13(x)
x = self.h14(x)
x = self.h15(x)
x = self.h16(x)
d1r = self.D1R(x)
x = self.h17(x)
x = self.h18(x)
d1c = self.D1C(x)
return d1r, d1c'''
class Discriminator1(Model):
def __init__(
self,
input_shape=(None, 256, 256, 3),
name='disc1',
**kwargs
):
super(Discriminator1, self).__init__(name=name, **kwargs)
self.d1 = D1(input_shape=input_shape)
def call(self, inputs):
image = inputs
d1r, d1c = self.d1(image)
d1_loss = d1_loss(d1r, d1c)
self.add_loss(d1_loss)
return out
When I call it in training, it throws a TypeError: expected string or byte-like object. I cannot figure what it is.
Any help? None of my functions are supposed to use strings
'''def generate_latent_noise(latent_dim, n_samples):
x_input = randn(latent_dim * n_samples)
x_input = x_input.reshape(n_samples, latent_dim)
return x_input'''
'''def generate_fake_samples(g, latent_dim, n_samples, y_i, y_l):
x_input = generate_latent_noise(latent_dim, n_samples)
X = g.predict(x_input)
y = zeros((n_samples, 1))
for i in range(n_samples-1):
intent = y_i[i]
bio = y_l[i]
return X, y, intent, bio'''
'''epochs = 200
opt = SGD(learning_rate=1e-3, momentum=0.99)
metric = Accuracy()
yi, yl = retrieve_target_labels('/content/drive/My Drive/Project/input.xlsx')
g = Generator(100)
d1 = D1((256, 256, 3))
d2 = D2((256, 256, 3))
gen = G_Model((256, 256, 3), 100, yi, yl)
disc1 = Discriminator1((256, 256, 3), 100)
disc2 = Discriminator2((256, 256, 3), 100)
art, yc_real, yi_real, yl_real =load_real_samples('/content/drive/MyDrive/Project/TrainSA.xlsx')
half_batch = yi.shape[0]
n_batch = half_batch * 2
batch_per_epoch = int(art.shape[0]/n_batch)
for epoch in range(epochs):
for batch in range(batch_per_epoch):
fake, y, yi, yl = generate_fake_samples(g, 100, half_batch, yi, yl)
real, y_real, c_real, i_real, l_real = generate_real_samples(art, half_batch, yc_real, yi_real, yl_real)
fake_image = tf.convert_to_tensor(fake)
d1r, d1c = d1(fake_image) #error!
d1_loss_fake = d1.losses
d1r, d1c = d1(real)
d1_loss = d1.losses
d2i_fake, d2l_fake = d2(fake_image)
d2_loss_fake = d2.losses
d2i, d2l = d2(real)
d2_loss = d2.losses
g_loss = gen.losses '''
It is a bit difficult to provide a minimal working example as the error is at the end of my code, after a lot of functions have been called, but I tried to include the ones that might be involved in the error.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-95-d4bb0da7c68f> in <module>()
23 #d1_loss_fake = d1.losses
24 real_image = tf.convert_to_tensor(real, dtype = tf.float32)
---> 25 d1r, d1c = disc1(real_image)
26 d1_loss = d1.losses
27 d2i_fake, d2l_fake = d2(fake_image)
3 frames
/tensorflow-1.15.2/python3.6/tensorflow_core/python/framework/ops.py in
name_scope(self, name)
4126 # Scopes created in the root must match the more restrictive
4127 # op name regex, which constrains the initial character.
-> 4128 if not _VALID_OP_NAME_REGEX.match(name):
4129 raise ValueError("'%s' is not a valid scope name" % name)
4130 old_stack = self._name_stack
TypeError: expected string or bytes-like object