from tensorflow.contrib.keras.python.keras import backend as K
from tensorflow.contrib.keras.python.keras import losses
x = np.random.random((10, 10)) * 2
y = np.random.randint(2, size=(10, 1))
input_tensor = Input(shape=(10, ))
bn_tensor = BatchNormalization()(input_tensor)
dp_tensor = Dropout(0.7)(bn_tensor)
final_tensor = Dense(1)(dp_tensor)
model = Model(input_tensor, final_tensor)
model.compile(optimizer='SGD', loss='mse')
# x, y won't get into batches for training here
loss_on_batch = model.train_on_batch(x, y) # dive in for details
"""
('Runs a single gradient update on a single batch of data.\n'
'\n'
'Arguments:\n'
' x: Numpy array of training data,\n'
' or list of Numpy arrays if the model has multiple inputs.\n'
' If all inputs in the model are named,\n'
' you can also pass a dictionary\n'
' mapping input names to Numpy arrays.\n'
' y: Numpy array of target data,\n'
' or list of Numpy arrays if the model has multiple outputs.\n'
' If all outputs in the model are named,\n'
' you can also pass a dictionary\n'
' mapping output names to Numpy arrays.\n'
' sample_weight: optional array of the same length as x, containing\n'
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
dprint(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
dprint(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Results will not be satisfactory. Keep training.'
)
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2 = roi_helpers.calc_iou(R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
def main():
training_images, training_labels, test_images, test_labels = load_dataset()
# plt.imshow(training_images[:,:,0], cmap='gray')
# plt.show()
perm_train = np.random.permutation(training_labels.size)
training_labels = training_labels[perm_train]
training_images = (training_images[perm_train, :, :] - 127.5) / 127.5
training_images = np.expand_dims(training_images, -1)
print(training_images.shape)
test_images = test_images / 255.0
test_images = np.expand_dims(test_images, -1)
# pdb.set_trace()
# training_labels = to_categorical(training_labels, NUM_CLASSES)
# test_labels = to_categorical(test_labels, NUM_CLASSES)
BATCH_SIZE = 32 * 8
WIDTH, HEIGHT = 28, 28
adam_lr = 0.0002
adam_beta_1 = 0.5
#####################################
### Defiining the Discriminator:
#####################################
input_D = Input(shape=(HEIGHT, WIDTH, 1), name='input_D')
x = Conv2D(filters=32,
kernel_size=3,
strides=(2, 2),
padding='same',
name='conv1_D')(input_D)
#x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters=32,
kernel_size=3,
strides=(2, 2),
padding='same',
name='conv2_D')(x)
#x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Flatten()(x)
x = Dense(128, activation='relu', name='dense1_D')(x)
output_D = Dense(1, activation='sigmoid', name='output_D')(x)
model_D = Model(inputs=input_D, outputs=output_D)
model_D.compile(loss='binary_crossentropy',
optimizer=tf.train.AdamOptimizer(learning_rate=adam_lr,
beta1=adam_beta_1),
metrics=['accuracy'])
#####################################
### Defiining the Generator:
#####################################
LATENT_SIZE = 100
input_G = Input(shape=(LATENT_SIZE, ), name='input_gen')
x = Dense(7 * 7 * 32, activation='linear', name='Dense1_G')(input_G)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Reshape((7, 7, 32))(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(filters=32,
kernel_size=3,
strides=(1, 1),
padding='same',
name='conv1_gen')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(filters=32,
kernel_size=3,
strides=(1, 1),
padding='same',
name='conv2_gen')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters=1,
kernel_size=1,
strides=(1, 1),
padding='same',
name='conv3_gen')(x)
img_G = Activation('tanh')(x)
model_G = Model(inputs=input_G, outputs=img_G)
model_G.compile(loss='binary_crossentropy',
optimizer=tf.train.AdamOptimizer(learning_rate=adam_lr,
beta1=adam_beta_1))
#####################################
### Defiining the Combined GAN:
#####################################
model_D.trainable = False # Since model_D is already compiled, thediscriminator model remains trainble,
# but here in the combined model it becomes non-trainable
input_main = Input(
shape=(LATENT_SIZE, ), name='input_main'
) # Note that this input should be different from the input to Generator
combined = Model(inputs=input_main, outputs=model_D(model_G(input_main)))
combined.compile(loss='binary_crossentropy',
optimizer=tf.train.AdamOptimizer(learning_rate=adam_lr,
beta1=adam_beta_1),
metrics=['accuracy'])
print(combined.summary())
#####################################
### Training:
#####################################
bar = InitBar()
N = training_images.shape[0]
for iter in range(100):
fake_input = np.random.randn(1, LATENT_SIZE)
fake_image = model_G.predict(fake_input)
loss_G, acc_G, loss_D, acc_D = 0, 0, 0, 0
steps = (int)(np.ceil(float(N) / float(BATCH_SIZE)))
for batch_iter in range(steps):
bar(100.0 * batch_iter / float(steps))
real_image, _ = get_batch(batch_iter, BATCH_SIZE / 2,
training_images, training_labels)
####################
## Discriminator Training
####################
# Note that if using BN layer in Discriminator, minibatch should contain only real images or fake images.
fake_input = np.random.randn(BATCH_SIZE / 2, LATENT_SIZE)
fake_image = model_G.predict(fake_input)
#real_image = get_real_mbatch(batch_sz=BATCH_SIZE/2, data=training_images)
agg_input = np.concatenate((fake_image, real_image), axis=0)
agg_output = np.zeros((BATCH_SIZE, ))
agg_output[BATCH_SIZE / 2:] = 1
perm = np.random.permutation(BATCH_SIZE)
agg_input = agg_input[perm]
agg_output = agg_output[perm]
#pdb.set_trace()
tr = model_D.train_on_batch(x=agg_input, y=agg_output)
loss_D += tr[0]
acc_D += tr[1]
#####################
## Generator Training
#####################
fake_input = np.random.randn(BATCH_SIZE, LATENT_SIZE)
fake_label = np.ones(BATCH_SIZE, )
tr = combined.train_on_batch(x=fake_input, y=fake_label)
loss_G += tr[0]
acc_G += tr[1]
print('\nG_loss = {}, G_acc = {}\nD_loss = {}, D_acc = {}'.format(
loss_G / float(steps), acc_G / float(steps), loss_D / float(steps),
acc_D / float(steps)))
for iter in range(10):
fake_input = np.random.randn(1, LATENT_SIZE)
fake_image = model_G.predict(fake_input)
plt.imshow(fake_image[0, :, :, 0])
plt.show()
