예제 #1
0
파일: Generator.py 프로젝트: pukekaka/dogs
    def sample(self, nb_samples):
        sampled_character_folders = random.sample(self.character_folders,
                                                  nb_samples)
        random.shuffle(sampled_character_folders)

        example_inputs = np.zeros(
            (self.batch_size, nb_samples * self.nb_samples_per_class,
             np.prod(self.img_size)),
            dtype=np.float32)
        example_outputs = np.zeros(
            (self.batch_size, nb_samples * self.nb_samples_per_class),
            dtype=np.float32
        )  #notice hardcoded np.float32 here and above, change it to something else in tf

        for i in range(self.batch_size):
            labels_and_images = get_shuffled_images(
                sampled_character_folders,
                range(nb_samples),
                nb_samples=self.nb_samples_per_class)
            sequence_length = len(labels_and_images)
            labels, image_files = zip(*labels_and_images)

            angles = np.random.uniform(-self.max_rotation,
                                       self.max_rotation,
                                       size=sequence_length)
            shifts = np.random.uniform(-self.max_shift,
                                       self.max_shift,
                                       size=sequence_length)

            example_inputs[i] = np.asarray([load_transform(filename, angle=angle, s=shift, size=self.img_size).flatten() \
                                            for (filename, angle, shift) in zip(image_files, angles, shifts)], dtype=np.float32)
            example_outputs[i] = np.asarray(labels, dtype=np.int32)

        return example_inputs, example_outputs
    def sample(self, nb_samples):
        sampled_character_folders = random.sample(self.character_folders, nb_samples)
        random.shuffle(sampled_character_folders)

        example_inputs = np.zeros((self.batch_size, nb_samples * self.nb_samples_per_class, np.prod(self.img_size)), dtype=np.float32)
        example_outputs = np.zeros((self.batch_size, nb_samples * self.nb_samples_per_class), dtype=np.float32)     #notice hardcoded np.float32 here and above, change it to something else in tf

        for i in range(self.batch_size):
            labels_and_images = get_shuffled_images(sampled_character_folders, range(nb_samples), nb_samples=self.nb_samples_per_class)
            sequence_length = len(labels_and_images)
            labels, image_files = zip(*labels_and_images)

            angles = np.random.uniform(-self.max_rotation, self.max_rotation, size=sequence_length)
            shifts = np.random.uniform(-self.max_shift, self.max_shift, size=sequence_length)

            example_inputs[i] = np.asarray([load_transform(filename, angle=angle, s=shift, size=self.img_size).flatten() \
                                            for (filename, angle, shift) in zip(image_files, angles, shifts)], dtype=np.float32)
            example_outputs[i] = np.asarray(labels, dtype=np.int32)

        return example_inputs, example_outputs
예제 #3
0
파일: omcontrol.py 프로젝트: qyhboy/file
def startom():   
    noise_input2 = tf.placeholder(tf.float32, shape=[None, latent_dim2])
    decoder2 = tf.matmul(tf.concat([noise_input2,y2], 1), weights2['decoder_h1']) + biases2['decoder_b1']
    decoder2 = tf.nn.tanh(decoder2)
    decoder2 = tf.matmul(decoder2, weights2['decoder_out']) + biases2['decoder_out']
    decoder2 = tf.nn.sigmoid(decoder2)
    n = 9
    noise_dim=2
    filename='/var/www/html/search/images/input.png'
    example_inputs = np.asarray([load_transform(filename, size=(28,28)).flatten() \
                                           ], dtype=np.float32)


    canvas_recon0 = np.empty((28 * (1), 28 * 1))
    canvas_recon1 = np.empty((28 * (1), 28 * 1))
    canvas_recon2 = np.empty((28 * (1), 28 * 1))
    canvas_recon3 = np.empty((28 * (1), 28 * 1))
    canvas_recon4 = np.empty((28 * (1), 28 * 1))
    canvas_recon5 = np.empty((28 * (1), 28 * 1))
    canvas_recon6 = np.empty((28 * (1), 28 * 1))
    canvas_recon7 = np.empty((28 * (1), 28 * 1))
    canvas_recon8 = np.empty((28 * (1), 28 * 1))


    for i in range(n):
        ztest = np.random.uniform(-1., 1., size=[1, noise_dim])
        g = sess.run(decoder2, feed_dict={noise_input2: ztest,y2:example_inputs })
        g = (g + 1.) / 2.
        g = -1 * (g - 1)
        if i==0:
            canvas_recon0[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon0, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==1:
            canvas_recon1[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon1, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==2:
            canvas_recon2[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon2, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==3:
            canvas_recon3[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon3, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==4:
            canvas_recon4[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon4, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==5:
            canvas_recon5[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon5, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==6:
            canvas_recon6[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon6, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==7:
            canvas_recon7[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon7, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )
        if i==8:
            canvas_recon8[ 0:(1) * 28,  0:(1) * 28] = g.reshape([28, 28])
            plt.figure(figsize=(1, 1))
            plt.axis('off')
            plt.imshow(canvas_recon8, origin="upper", cmap="gray")
            plt.savefig('/var/www/html/search/images/out%d.png'%(i+1) )