def fit(self, X):
d_costs = []
g_costs = []
N = len(X)
n_batches = N // BATCH_SIZE
total_iters = 0
for i in range(0, EPOCHS):
print("epoch: ", i)
np.random.shuffle(X)
for j in range(0, n_batches):
t0 = datetime.now()
if (type(X[0]) is str):
batch = util.files2images(X[j * BATCH_SIZE:((j + 1) *
BATCH_SIZE)])
else:
batch = X[j * BATCH_SIZE:(j + 1) * BATCH_SIZE]
Z = np.random.uniform(-1,
1,
size=(BATCH_SIZE, self.latent_dims))
_, d_cost, d_acc = self.sess.run(
(self.d_train_op, self.d_cost, self.d_accuracy),
feed_dict={
self.X: batch,
self.Z: Z
})
d_costs.append(d_cost)
_, g_cost1 = self.sess.run((self.g_train_op, self.g_cost),
feed_dict={self.Z: Z})
_, g_cost2 = self.sess.run((self.g_train_op, self.g_cost),
feed_dict={self.Z: Z})
g_costs.append((g_cost1 + g_cost2) / 2)
print("batch %d/%d - dt: %s - d_acc: %.2f" %
(j + 1, n_batches, datetime.now() - t0))
total_iters += 1
if total_iters % SAVE_SAMPLE_PERIOD == 0:
print("saving sample...")
samples = self.sample(64)
d = self.img_length
if samples.shape[-1] == 1:
samples = samples.reshape(64, d, d)
flat_image = np.empty((8 * d, 8 * d))
k = 0
for a in range(0, 8):
for b in range(0, 8):
flat_image[a * d:(a + 1) * d, b * d:(b + 1) *
d] = samples[k].reshape(d, d)
k += 1
else:
flat_image = np.empty((8 * d, 8 * d, 3))
k = 0
for a in range(0, 8):
for b in range(0, 8):
flat_image[a * d:(a + 1) * d,
b * d:(b + 1) * d] = samples[k]
k += 1
sp.misc.imsave(
'samples/samples_at_iter_%d.png' % total_iters,
(flat_image + 1) / 2)
plt.clf()
plt.plot(d_costs, label='discriminator cost')
plt.plot(g_costs, label='generator cost')
plt.legend()
plt.savefig('cost_vs_iteration.png')
def fit(self, X):
d_costs = []
g_costs = []
N = len(X)
n_batches = N // BATCH_SIZE
total_iters = 0
for i in range(EPOCHS):
print("epoch:", i)
np.random.shuffle(X)
for j in range(n_batches):
t0 = datetime.now()
if type(X[0]) is str:
# is celeb dataset
batch = util.files2images(X[j * BATCH_SIZE:(j + 1) *
BATCH_SIZE])
else:
# is mnist dataset
batch = X[j * BATCH_SIZE:(j + 1) * BATCH_SIZE]
Z = np.random.uniform(-1,
1,
size=(BATCH_SIZE, self.latent_dims))
# train the discriminator
_, d_cost, d_acc = self.sess.run(
(self.d_train_op, self.d_cost, self.d_accuracy),
feed_dict={
self.X: batch,
self.Z: Z,
self.batch_sz: BATCH_SIZE
},
)
d_costs.append(d_cost)
# train the generator
_, g_cost1 = self.sess.run(
(self.g_train_op, self.g_cost),
feed_dict={
self.Z: Z,
self.batch_sz: BATCH_SIZE
},
)
# g_costs.append(g_cost1)
_, g_cost2 = self.sess.run(
(self.g_train_op, self.g_cost),
feed_dict={
self.Z: Z,
self.batch_sz: BATCH_SIZE
},
)
g_costs.append((g_cost1 + g_cost2) / 2) # just use the avg
print(" batch: %d/%d - dt: %s - d_acc: %.2f" %
(j + 1, n_batches, datetime.now() - t0, d_acc))
# save samples periodically
total_iters += 1
if total_iters % SAVE_SAMPLE_PERIOD == 0:
print("saving a sample...")
samples = self.sample(64) # shape is (64, D, D, color)
# for convenience
d = self.img_length
if samples.shape[-1] == 1:
# if color == 1, we want a 2-D image (N x N)
samples = samples.reshape(64, d, d)
flat_image = np.empty((8 * d, 8 * d))
k = 0
for i in range(8):
for j in range(8):
flat_image[i * d:(i + 1) * d, j * d:(j + 1) *
d] = samples[k].reshape(d, d)
k += 1
# plt.imshow(flat_image, cmap='gray')
else:
# if color == 3, we want a 3-D image (N x N x 3)
flat_image = np.empty((8 * d, 8 * d, 3))
k = 0
for i in range(8):
for j in range(8):
flat_image[i * d:(i + 1) * d,
j * d:(j + 1) * d] = samples[k]
k += 1
# plt.imshow(flat_image)
# plt.savefig('samples/samples_at_iter_%d.png' % total_iters)
sp.misc.imsave(
'samples/samples_at_iter_%d.png' % total_iters,
flat_image,
)
# save a plot of the costs
plt.clf()
plt.plot(d_costs, label='discriminator cost')
plt.plot(g_costs, label='generator cost')
plt.legend()
plt.savefig('cost_vs_iteration.png')
def fit(self, X):
d_costs = []
g_costs = []
N = len(X)
n_batches = N // BATCH_SIZE
total_iters = 0
for i in range(EPOCHS):
print("epoch:", i)
np.random.shuffle(X)
for j in range(n_batches):
t0 = datetime.now()
if type(X[0]) is str:
# is celeb dataset
batch = util.files2images(
X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]
)
else:
# is mnist dataset
batch = X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]
Z = np.random.uniform(-1, 1, size=(BATCH_SIZE, self.latent_dims))
# train the discriminator
_, d_cost, d_acc = self.sess.run(
(self.d_train_op, self.d_cost, self.d_accuracy),
feed_dict={self.X: batch, self.Z: Z, self.batch_sz: BATCH_SIZE},
)
d_costs.append(d_cost)
# train the generator
_, g_cost1 = self.sess.run(
(self.g_train_op, self.g_cost),
feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},
)
# g_costs.append(g_cost1)
_, g_cost2 = self.sess.run(
(self.g_train_op, self.g_cost),
feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},
)
g_costs.append((g_cost1 + g_cost2)/2) # just use the avg
print(" batch: %d/%d - dt: %s - d_acc: %.2f" % (j+1, n_batches, datetime.now() - t0, d_acc))
# save samples periodically
total_iters += 1
if total_iters % SAVE_SAMPLE_PERIOD == 0:
print("saving a sample...")
samples = self.sample(64) # shape is (64, D, D, color)
# for convenience
d = self.img_length
if samples.shape[-1] == 1:
# if color == 1, we want a 2-D image (N x N)
samples = samples.reshape(64, d, d)
flat_image = np.empty((8*d, 8*d))
k = 0
for i in range(8):
for j in range(8):
flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].reshape(d, d)
k += 1
# plt.imshow(flat_image, cmap='gray')
else:
# if color == 3, we want a 3-D image (N x N x 3)
flat_image = np.empty((8*d, 8*d, 3))
k = 0
for i in range(8):
for j in range(8):
flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k]
k += 1
# plt.imshow(flat_image)
# plt.savefig('samples/samples_at_iter_%d.png' % total_iters)
sp.misc.imsave(
'samples/samples_at_iter_%d.png' % total_iters,
flat_image,
)
# save a plot of the costs
plt.clf()
plt.plot(d_costs, label='discriminator cost')
plt.plot(g_costs, label='generator cost')
plt.legend()
plt.savefig('cost_vs_iteration.png')
