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I'm trying to generate mnist dataset images. Here is my code:
fns.py:
import math
import numpy as np
def combine_images(generated_images):
total,width,height = generated_images.shape[:-1]
cols = int(math.sqrt(total))
rows = math.ceil(float(total)/cols)
combined_image = np.zeros((height*rows, width*cols),
dtype=generated_images.dtype)
for index, image in enumerate(generated_images):
i = int(index/cols)
j = index % cols
combined_image[width*i:width*(i+1), height*j:height*(j+1)] = image[:, :, 0]
return combined_image
def show_progress(epoch, batch, g_loss, d_loss, g_acc, d_acc):
msg = "epoch: {}, batch: {}, g_loss: {}, d_loss: {}, g_accuracy: {}, d_accuracy: {}"
print(msg.format(epoch, batch, g_loss, d_loss, g_acc, d_acc))
main.py:
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Activation, Reshape
from tensorflow.python.keras.layers import BatchNormalization
from tensorflow.python.keras.layers import UpSampling2D, Conv2D
from tensorflow.python.keras.layers import ELU
from tensorflow.python.keras.layers import Flatten, Dropout
from tensorflow.python.keras.optimizers import Adam
from tensorflow.python.keras.datasets import mnist
import os
from PIL import Image
from fns import *
def generator(input_dimension=100, units=1024, activation_function='relu'):
model = Sequential()
model.add(Dense(input_dim=input_dimension, units=units))
model.add(BatchNormalization())
model.add(Activation(activation_function))
model.add(Dense(128*7*7))
model.add(BatchNormalization())
model.add(Activation(activation_function))
model.add(Reshape((7,7,128), input_shape=(128*7*7,)))
model.add(UpSampling2D((2,2)))
model.add(Conv2D(64, (5,5), padding='same'))
model.add(BatchNormalization())
model.add(Activation(activation_function))
model.add(UpSampling2D((2,2)))
model.add(Conv2D(1, (5,5), padding='same'))
model.add(Activation('tanh'))
print(model.summary())
return model
def discriminator(input_shape=(28,28,1), nb_filter=64):
model = Sequential()
model.add(Conv2D(nb_filter, (5,5), strides=(2,2), padding='same', input_shape=input_shape))
model.add(BatchNormalization())
model.add(ELU())
model.add(Conv2D(2*nb_filter, (5,5), strides=(2,2)))
model.add(BatchNormalization())
model.add(ELU())
model.add(Flatten())
model.add(Dense(4*nb_filter))
model.add(BatchNormalization())
model.add(ELU())
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
print(model.summary())
return model
batch_size = 32
num_epoch = 50
learning_rate = 0.0002
image_path = 'images/'
if not os.path.exists(image_path):
os.mkdir(image_path)
def train():
(x_train, y_train), (_, _) = mnist.load_data()
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
g = generator()
d = discriminator()
optimize = Adam(lr=learning_rate, beta_1=0.5)
d.trainable = True
d.compile(
loss='binary_crossentropy',
metrics=['accuracy'],
optimizer=optimize)
d.trainable = False
dcgan = Sequential([g, d])
dcgan.compile(
loss='binary_crossentropy',
metrics=['accuracy'],
optimizer=optimize)
num_batches = x_train.shape[0] // batch_size #return integer
gen_img = np.array([np.random.uniform(-1, 1, 100) for _ in range(49)])
y_d_true = [1] * batch_size
y_d_gen = [0] * batch_size
y_g = [1] * batch_size
for epoch in range(num_epoch):
for i in range(num_batches):
x_d_batch = x_train[i*batch_size:(i+1)*batch_size]
x_g = np.array([np.random.normal(0, 0.5, 100) for _ in range(batch_size)])
x_d_gen = g.predict(x_g)
d_loss = d.train_on_batch(x_d_batch, y_d_true)
d_loss = d.train_on_batch(x_d_gen, y_d_gen)
g_loss = dcgan.train_on_batch(x_g, y_g)
show_progress(epoch, i, g_loss[0], d_loss[0], g_loss[1], d_loss[1])
image = combine_images(g.predict(gen_img))
image = image * 127.5 + 127*5
image.fromarray(image.astype(np.uint8)).save(image_path + "%03d.png" % (epoch))
if __name__ == '__main__':
train()
When I run this script, it gives this error:
Traceback (most recent call last):
File "e:/Programming/Tensorflow/tensorflow-ile-goruntu-isleme/gans/main.py", line 113, in <module>
train()
File "e:/Programming/Tensorflow/tensorflow-ile-goruntu-isleme/gans/main.py", line 81, in train
optimizer=optimize)
File "D:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 325, in compile
self._validate_compile(optimizer, metrics, **kwargs)
File "D:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1560, in _validate_compile
'`tf.compat.v1.keras` Optimizer (', optimizer, ') is '
ValueError: ('`tf.compat.v1.keras` Optimizer (', <tensorflow.python.keras.optimizers.Adam object at 0x00000272008C7B48>, ') is not supported when eager execution is enabled. Use a `tf.keras` Optimizer instead, or disable eager execution.')
I've searched so many pages, but couldn't find a satisfying solution.
498
By default tensorflow version 2.x are eager execution enabled.
I was able to reproduce your error in tensorflow version 2.2.0 in the below program. Error appeared when I import optimizer using from tensorflow.python.keras.optimizers import Adam in the program -
Code to reproduce the error -
%tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.optimizers import Adam
#from tensorflow.keras.optimizers import Adam
from tensorflow.keras import backend as K
import os
import numpy as np
import matplotlib.pyplot as plt
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()
train_images = train_images[:500]
train_labels = train_labels[:500]
test_images = test_images[:50]
test_labels = test_labels[:50]
model = Sequential([
Conv2D(16, 3, padding='same', activation='relu', input_shape=(32, 32, 3)),
MaxPooling2D(),
Conv2D(32, 3, padding='same', activation='relu'),
MaxPooling2D(),
Conv2D(64, 3, padding='same', activation='relu'),
MaxPooling2D(),
Flatten(),
Dense(512, activation='relu'),
Dense(10)
])
lr = 0.01
adam = Adam(lr)
# Define the Gradient Fucntion
epoch_gradient = []
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
with tf.GradientTape() as tape:
logits = model(train_images, training=True)
loss = loss_fn(train_labels, logits)
grad = tape.gradient(loss, model.trainable_weights)
model.optimizer.apply_gradients(zip(grad, model.trainable_variables))
epoch_gradient.append(grad)
gradcalc = GradientCalcCallback()
# Define the Required Callback Function
class printlearningrate(tf.keras.callbacks.Callback):
def on_epoch_begin(self, epoch, logs={}):
optimizer = self.model.optimizer
lr = K.eval(optimizer.lr)
Epoch_count = epoch + 1
print('\n', "Epoch:", Epoch_count, ', LR: {:.2f}'.format(lr))
printlr = printlearningrate()
def scheduler(epoch):
optimizer = model.optimizer
return K.eval(optimizer.lr + 0.01)
updatelr = tf.keras.callbacks.LearningRateScheduler(scheduler)
model.compile(optimizer=adam,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs = 10
history = model.fit(train_images, train_labels, epochs=epochs, batch_size=len(train_images),
validation_data=(test_images, test_labels),
callbacks = [printlr,updatelr,gradcalc])
# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epochs)
print("Gradient Array has the shape:",gradient.shape)
Output -
2.2.0
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-0f0fef768c1c> in <module>()
70 model.compile(optimizer=adam,
71 loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
---> 72 metrics=['accuracy'])
73
74 epochs = 10
1 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _validate_compile(self, optimizer, metrics, **kwargs)
1558 for opt in nest.flatten(optimizer)):
1559 raise ValueError(
-> 1560 '`tf.compat.v1.keras` Optimizer (', optimizer, ') is '
1561 'not supported when eager execution is enabled. Use a '
1562 '`tf.keras` Optimizer instead, or disable eager '
ValueError: ('`tf.compat.v1.keras` Optimizer (', <tensorflow.python.keras.optimizers.Adam object at 0x7fce341a15c0>, ') is not supported when eager execution is enabled. Use a `tf.keras` Optimizer instead, or disable eager execution.')
Solution -
Modify,
from tensorflow.python.keras.optimizers import Adam
to
from tensorflow.keras.optimizers import Adam
Note : Also kindly import other libraries from tensorflow.keras instead of tensorflow.python.keras.
Fixed Code -
%tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import backend as K
import os
import numpy as np
import matplotlib.pyplot as plt
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()
train_images = train_images[:500]
train_labels = train_labels[:500]
test_images = test_images[:50]
test_labels = test_labels[:50]
model = Sequential([
Conv2D(16, 3, padding='same', activation='relu', input_shape=(32, 32, 3)),
MaxPooling2D(),
Conv2D(32, 3, padding='same', activation='relu'),
MaxPooling2D(),
Conv2D(64, 3, padding='same', activation='relu'),
MaxPooling2D(),
Flatten(),
Dense(512, activation='relu'),
Dense(10)
])
lr = 0.01
adam = Adam(lr)
# Define the Gradient Fucntion
epoch_gradient = []
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
with tf.GradientTape() as tape:
logits = model(train_images, training=True)
loss = loss_fn(train_labels, logits)
grad = tape.gradient(loss, model.trainable_weights)
model.optimizer.apply_gradients(zip(grad, model.trainable_variables))
epoch_gradient.append(grad)
gradcalc = GradientCalcCallback()
# Define the Required Callback Function
class printlearningrate(tf.keras.callbacks.Callback):
def on_epoch_begin(self, epoch, logs={}):
optimizer = self.model.optimizer
lr = K.eval(optimizer.lr)
Epoch_count = epoch + 1
print('\n', "Epoch:", Epoch_count, ', LR: {:.2f}'.format(lr))
printlr = printlearningrate()
def scheduler(epoch):
optimizer = model.optimizer
return K.eval(optimizer.lr + 0.01)
updatelr = tf.keras.callbacks.LearningRateScheduler(scheduler)
model.compile(optimizer=adam,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs = 10
history = model.fit(train_images, train_labels, epochs=epochs, batch_size=len(train_images),
validation_data=(test_images, test_labels),
callbacks = [printlr,updatelr,gradcalc])
# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epochs)
print("Gradient Array has the shape:",gradient.shape)
Output -
2.2.0
Epoch: 1 , LR: 0.01
Epoch 1/10
1/1 [==============================] - 0s 471ms/step - loss: 71.8890 - accuracy: 0.0740 - val_loss: 3694.5439 - val_accuracy: 0.0800 - lr: 0.0200
Epoch: 2 , LR: 0.02
Epoch 2/10
1/1 [==============================] - 0s 330ms/step - loss: 113.0054 - accuracy: 0.1060 - val_loss: 172.5451 - val_accuracy: 0.0600 - lr: 0.0300
Epoch: 3 , LR: 0.03
Epoch 3/10
1/1 [==============================] - 0s 331ms/step - loss: 3.3038 - accuracy: 0.0960 - val_loss: 280.0600 - val_accuracy: 0.1800 - lr: 0.0400
Epoch: 4 , LR: 0.04
Epoch 4/10
1/1 [==============================] - 0s 339ms/step - loss: 3.2624 - accuracy: 0.0940 - val_loss: 2.3644 - val_accuracy: 0.1800 - lr: 0.0500
Epoch: 5 , LR: 0.05
Epoch 5/10
1/1 [==============================] - 0s 335ms/step - loss: 2.3810 - accuracy: 0.1120 - val_loss: 2.3599 - val_accuracy: 0.1600 - lr: 0.0600
Epoch: 6 , LR: 0.06
Epoch 6/10
1/1 [==============================] - 0s 339ms/step - loss: 2.3205 - accuracy: 0.1120 - val_loss: 2.3333 - val_accuracy: 0.0600 - lr: 0.0700
Epoch: 7 , LR: 0.07
Epoch 7/10
1/1 [==============================] - 0s 337ms/step - loss: 2.3178 - accuracy: 0.1300 - val_loss: 2.3435 - val_accuracy: 0.0600 - lr: 0.0800
Epoch: 8 , LR: 0.08
Epoch 8/10
1/1 [==============================] - 0s 338ms/step - loss: 2.3028 - accuracy: 0.1300 - val_loss: 2.3059 - val_accuracy: 0.0600 - lr: 0.0900
Epoch: 9 , LR: 0.09
Epoch 9/10
1/1 [==============================] - 0s 336ms/step - loss: 2.2990 - accuracy: 0.1300 - val_loss: 2.3093 - val_accuracy: 0.1000 - lr: 0.1000
Epoch: 10 , LR: 0.10
Epoch 10/10
1/1 [==============================] - 0s 339ms/step - loss: 2.3033 - accuracy: 0.1020 - val_loss: 2.3161 - val_accuracy: 0.1000 - lr: 0.1100
Total number of epochs run: 10
Gradient Array has the shape: (10, 10)
Hope this answers your question. Happy Learning.
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