I am brand new to Python and programming. I am trying to code a simple GAN to use Keras datasets (see hyperlink to tutorial below).
I am receiving two warnings followed by an error:
TypeError: 'float' object cannot be interpreted as an integer.
Any help would be much appreciated.
Details:
Python 3.7.1, Mac OS High Sierra 10.13.6. I am using IDLE for the Python code and running the program through the terminal.
Error
WARNING:tensorflow:From /Users/darren/miniconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating: Colocations handled automatically by placer.
WARNING:tensorflow:From /Users/darren/miniconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use rate instead of keep_prob. Rate should be set to rate = 1 - keep_prob.
--------------- Epoch 1 ---------------
Traceback (most recent call last):
File "gan.py", line 91, in train(400, 128) File "gan.py", line 75, in train for _ in tqdm(range(batch_count)): TypeError: 'float' object cannot be interpreted as an integer
Code:
import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import mnist
from keras.optimizers import Adam
from keras import initializers
os.environ["KERAS_BACKEND"] = "tensorflow"
np.random.seed(10)
random_dim = 100
def load_minst_data():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = (x_train.astype(np.float32) - 127.5)/127.5
x_train = x_train.reshape(60000, 784)
return (x_train, y_train, x_test, y_test)
def get_optimizer():
return Adam(lr=0.0002, beta_1=0.5)
def get_generator(optimizer):
generator = Sequential()
generator.add(Dense(256, input_dim=random_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
generator.add(LeakyReLU(0.2))
generator.add(Dense(512))
generator.add(LeakyReLU(0.2))
generator.add(Dense(1024))
generator.add(LeakyReLU(0.2))
generator.add(Dense(784, activation='tanh'))
generator.compile(loss='binary_crossentropy', optimizer=optimizer)
return generator
def get_discriminator(optimizer):
discriminator = Sequential()
discriminator.add(Dense(1024, input_dim=784, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(512))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(256))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(1, activation='sigmoid'))
discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
return discriminator
def get_gan_network(discriminator, random_dim, generator, optimizer):
discriminator.trainable = False
gan_input = Input(shape=(random_dim,))
x = generator(gan_input)
gan_output = discriminator(x)
gan = Model(inputs=gan_input, outputs=gan_output)
gan.compile(loss='binary_crossentropy', optimizer=optimizer)
return gan
def plot_generated_images(epoch, generator, examples=100, dim=(10, 10), figsize=(10, 10)):
noise = np.random.normal(0, 1, size=[examples, random_dim])
generated_images = generator.predict(noise)
generated_images = generated_images.reshape(examples, 28, 28)
plt.figure(figsize=figsize)
for i in range(generated_images.shape[0]):
plt.subplot(dim[0], dim[1], i+1)
plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')
plt.axis('off')
plt.tight_layout()
plt.savefig('gan_generated_image_epoch_%d.png' % epoch)
def train(epochs=1, batch_size=128):
x_train, y_train, x_test, y_test = load_minst_data()
batch_count = x_train.shape[0] / batch_size
adam = get_optimizer()
generator = get_generator(adam)
discriminator = get_discriminator(adam)
gan = get_gan_network(discriminator, random_dim, generator, adam)
for e in range(1, epochs+1):
print ('-'*15, 'Epoch %d' % e, '-'*15)
for _ in tqdm(range(batch_count)):
noise = np.random.normal(0, 1, size=[batch_size, random_dim])
image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
generated_images = generator.predict(noise)|
X = np.concatenate([image_batch, generated_images])
y_dis = np.zeros(2*batch_size)
y_dis[:batch_size] = 0.9
discriminator.trainable = True
discriminator.train_on_batch(X, y_dis)
noise = np.random.normal(0, 1, size=[batch_size, random_dim])
y_gen = np.ones(batch_size)
discriminator.trainable = False
gan.train_on_batch(noise, y_gen)
if e == 1 or e % 20 == 0:
plot_generated_images(e, generator)
if __name__ == '__main__':
train(400, 128)
for _ in...., and print the type of the variable batch_count. For the loop to work, it should be anint– entropyprint(type(batch_count)). Also consider reading this: docs.python.org/3/library/pdb.html – entropy