# get our mnist data, and force it to be of shape (., 1, 28, 28) with # we only want to be able to train generation for the combined modelД¬ombined = Model(, ) pile(optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1), Optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1), # batch and latent size taken from the paper # (name=auxiliary) is the class that the discriminator thinks the imageДЇake = Dense(1, activation='sigmoid', name='generation')(features) # fake oder nicht fakeДЄux = Dense(num_classes, activation='softmax', name='auxiliary')(features) #welche klasse ist es # thinks the image that is being shown is fake, and the second output # first output (name=generation) is whether or not the discriminator # build a relatively standard conv net, with LeakyReLUs as suggested inД¬nn.add(Conv2D(32, 3, padding='same', strides=2,Д¬nn.add(Conv2D(64, 3, padding='same', strides=1))Д¬nn.add(Conv2D(128, 3, padding='same', strides=2))Д¬nn.add(Conv2D(256, 3, padding='same', strides=1)) K.set_image_data_format('channels_first') Prefer the TensorFlow backend if you plan on iterating,ДЄs the compilation time can be a blocker using Theano.ДЇrom keras.layers import Input, Dense, Reshape, Flatten, Embedding, DropoutДЇrom _activations import LeakyReLUДЇrom import UpSampling2D, Conv2DДЇrom keras.models import Sequential, ModelДЇrom _utils import Progbar

You should use a GPU, as the convolution-heavy operations are You should start to see reasonable images after ~5 epochs, and good imagesД«y ~15 epochs.

Train an Auxiliary Classifier Generative Adversarial Network (ACGAN) on the At the end the new points with label '1' are not close to the other points with label '1'. But i think it doesn't change a thing, if I multiply the noise in the 2 dimensional space or the 100 dimensional space. The latent_size in my pictures is 2, because I wanted to visualize them. So how can the network know, what digit it should draw? EDIT: So the network knows, what new array represents what number.Д«ut the output of np.multiply(noise,embedded_label) is this: He is multiplying the latent-arrays(the noise) with the embedding(of the labels): If I give the network more latent-arrays and labels. After the network is embedding the labels. The Input is a latent-array noise = np.random.uniform(-1, 1, (batch_size, latent_size))ДЄnd the labels are just generated randomly. # hadamard product between z-space and a class conditional embedding Image_class = Input(shape=(1,), dtype='int32')Д¬ls = Flatten()(Embedding(num_classes, latent_size,Д®mbeddings_initializer='glorot_normal')(image_class)) # this is the z space commonly refered to in GAN papers # label drawn from P_c, to image space (., 1, 28, 28)Д¬nn.add(Dense(1024, input_dim=latent_size, activation='relu'))Д¬nn.add(Dense(128 * 7 * 7, activation='relu')) # we will map a pair of (z, L), where z is a latent vector and L is a Here is the Generator: def build_generator(latent_size): But I cant understand how it knows, which digit it should draw.

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