Unable to identify Tuple index error

[appmkl]

Hello !
I've been wanting to create a DCGAN following this tutorial.
I've got the whole thing up and running on GCP, with my own image dataset (trying to get the GAN to generate satellite imagery).

This is the tutorial I followed, step by step.
https://towardsdatascience.com/image-generator-drawing-cartoons-with-generative-adversarial-networks-45e814ca9b6b


However, once I run the file, I get this error message.

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-14-db5a32a0c488> in <module>
      9 
     10 with tf.Graph().as_default():
---> 11     train(get_batches(input_images), input_images.shape)

<ipython-input-11-73fe85d9d0bc> in train(get_batches, data_shape, checkpoint_to_load)
      1 def train(get_batches, data_shape, checkpoint_to_load=None):
      2     input_images, input_z, lr_G, lr_D = model_inputs(data_shape[1:], NOISE_SIZE)
----> 3     d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
      4     d_opt, g_opt = model_optimizers(d_loss, g_loss)
      5 

IndexError: tuple index out of range

I don't understand why I'm getting this error message, as I've followed the tutorial and copied the same code...
Any help greatly appreciated, been trying to figure this out for a week now, but relatively new to python.

Thank you !!

[Larz60+]

Please show your code (not tutorials)

[appmkl]

Please show your code (not tutorials)

Here you go ! Quite long..

import os
import time
import tensorflow as tf
import numpy as np
from glob import glob
import datetime
import random
from PIL import Image
import matplotlib.pyplot as plt
%matplotlib inline

in2

def generator(z, output_channel_dim, training):
    with tf.variable_scope("generator", reuse= not training):
        
        # 8x8x1024
        fully_connected = tf.layers.dense(z, 8*8*1024)
        fully_connected = tf.reshape(fully_connected, (-1, 8, 8, 1024))
        fully_connected = tf.nn.leaky_relu(fully_connected)
​
        # 8x8x1024 -> 16x16x512
        trans_conv1 = tf.layers.conv2d_transpose(inputs=fully_connected,
                                                 filters=512,
                                                 kernel_size=[5,5],
                                                 strides=[2,2],
                                                 padding="SAME",
                                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                                 name="trans_conv1")
        batch_trans_conv1 = tf.layers.batch_normalization(inputs = trans_conv1,
                                                          training=training,
                                                          epsilon=EPSILON,
                                                          name="batch_trans_conv1")
        trans_conv1_out = tf.nn.leaky_relu(batch_trans_conv1,
                                           name="trans_conv1_out")
        
        # 16x16x512 -> 32x32x256
        trans_conv2 = tf.layers.conv2d_transpose(inputs=trans_conv1_out,
                                                 filters=256,
                                                 kernel_size=[5,5],
                                                 strides=[2,2],
                                                 padding="SAME",
                                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                                 name="trans_conv2")
        batch_trans_conv2 = tf.layers.batch_normalization(inputs = trans_conv2,
                                                          training=training,
                                                          epsilon=EPSILON,
                                                          name="batch_trans_conv2")
        trans_conv2_out = tf.nn.leaky_relu(batch_trans_conv2,
                                           name="trans_conv2_out")
        
        # 32x32x256 -> 64x64x128
        trans_conv3 = tf.layers.conv2d_transpose(inputs=trans_conv2_out,
                                                 filters=128,
                                                 kernel_size=[5,5],
                                                 strides=[2,2],
                                                 padding="SAME",
                                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                                 name="trans_conv3")
        batch_trans_conv3 = tf.layers.batch_normalization(inputs = trans_conv3,
                                                          training=training,
                                                          epsilon=EPSILON,
                                                          name="batch_trans_conv3")
        trans_conv3_out = tf.nn.leaky_relu(batch_trans_conv3,
                                           name="trans_conv3_out")
        
        # 64x64x128 -> 128x128x64
        trans_conv4 = tf.layers.conv2d_transpose(inputs=trans_conv3_out,
                                                 filters=64,
                                                 kernel_size=[5,5],
                                                 strides=[2,2],
                                                 padding="SAME",
                                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                                 name="trans_conv4")
        batch_trans_conv4 = tf.layers.batch_normalization(inputs = trans_conv4,
                                                          training=training,
                                                          epsilon=EPSILON,
                                                          name="batch_trans_conv4")
        trans_conv4_out = tf.nn.leaky_relu(batch_trans_conv4,
                                           name="trans_conv4_out")
        
        # 128x128x64 -> 128x128x3
        logits = tf.layers.conv2d_transpose(inputs=trans_conv4_out,
                                            filters=3,
                                            kernel_size=[5,5],
                                            strides=[1,1],
                                            padding="SAME",
                                            kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                            name="logits")
        out = tf.tanh(logits, name="out")
        return out

In [3]:

def discriminator(x, reuse):
    with tf.variable_scope("discriminator", reuse=reuse): 
        
        # 128*128*3 -> 64x64x64 
        conv1 = tf.layers.conv2d(inputs=x,
                                 filters=64,
                                 kernel_size=[5,5],
                                 strides=[2,2],
                                 padding="SAME",
                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                 name='conv1')
        batch_norm1 = tf.layers.batch_normalization(conv1,
                                                    training=True,
                                                    epsilon=EPSILON,
                                                    name='batch_norm1')
        conv1_out = tf.nn.leaky_relu(batch_norm1,
                                     name="conv1_out")
        
        # 64x64x64-> 32x32x128 
        conv2 = tf.layers.conv2d(inputs=conv1_out,
                                 filters=128,
                                 kernel_size=[5, 5],
                                 strides=[2, 2],
                                 padding="SAME",
                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                 name='conv2')
        batch_norm2 = tf.layers.batch_normalization(conv2,
                                                    training=True,
                                                    epsilon=EPSILON,
                                                    name='batch_norm2')
        conv2_out = tf.nn.leaky_relu(batch_norm2,
                                     name="conv2_out")
        
        # 32x32x128 -> 16x16x256  
        conv3 = tf.layers.conv2d(inputs=conv2_out,
                                 filters=256,
                                 kernel_size=[5, 5],
                                 strides=[2, 2],
                                 padding="SAME",
                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                 name='conv3')
        batch_norm3 = tf.layers.batch_normalization(conv3,
                                                    training=True,
                                                    epsilon=EPSILON,
                                                    name='batch_norm3')
        conv3_out = tf.nn.leaky_relu(batch_norm3,
                                     name="conv3_out")
        
        # 16x16x256 -> 16x16x512
        conv4 = tf.layers.conv2d(inputs=conv3_out,
                                 filters=512,
                                 kernel_size=[5, 5],
                                 strides=[1, 1],
                                 padding="SAME",
                                 kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                 name='conv4')
        batch_norm4 = tf.layers.batch_normalization(conv4,
                                                    training=True,
                                                    epsilon=EPSILON,
                                                    name='batch_norm4')
        conv4_out = tf.nn.leaky_relu(batch_norm4,
                                     name="conv4_out")
        
        # 16x16x512 -> 8x8x1024
        conv5 = tf.layers.conv2d(inputs=conv4_out,
                                filters=1024,
                                kernel_size=[5, 5],
                                strides=[2, 2],
                                padding="SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=WEIGHT_INIT_STDDEV),
                                name='conv5')
        batch_norm5 = tf.layers.batch_normalization(conv5,
                                                    training=True,
                                                    epsilon=EPSILON,
                                                    name='batch_norm5')
        conv5_out = tf.nn.leaky_relu(batch_norm5,
                                     name="conv5_out")
​
        flatten = tf.reshape(conv5_out, (-1, 8*8*1024))
        logits = tf.layers.dense(inputs=flatten,
                                 units=1,
                                 activation=None)
        out = tf.sigmoid(logits)
        return out, logits

In [4]:

def model_loss(input_real, input_z, output_channel_dim):
    g_model = generator(input_z, output_channel_dim, True)
​
    noisy_input_real = input_real + tf.random_normal(shape=tf.shape(input_real),
                                                     mean=0.0,
                                                     stddev=random.uniform(0.0, 0.1),
                                                     dtype=tf.float32)
    
    d_model_real, d_logits_real = discriminator(noisy_input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                                         labels=tf.ones_like(d_model_real)*random.uniform(0.9, 1.0)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                                         labels=tf.zeros_like(d_model_fake)))
    d_loss = tf.reduce_mean(0.5 * (d_loss_real + d_loss_fake))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                                    labels=tf.ones_like(d_model_fake)))
    return d_loss, g_loss

In [5]:

def model_optimizers(d_loss, g_loss):
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith("generator")]
    d_vars = [var for var in t_vars if var.name.startswith("discriminator")]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    gen_updates = [op for op in update_ops if op.name.startswith('generator')]
    
    with tf.control_dependencies(gen_updates):
        d_train_opt = tf.train.AdamOptimizer(learning_rate=LR_D, beta1=BETA1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate=LR_G, beta1=BETA1).minimize(g_loss, var_list=g_vars)  
    return d_train_opt, g_train_opt

In [6]:

def model_inputs(real_dim, z_dim):
    inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='inputs_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name="input_z")
    learning_rate_G = tf.placeholder(tf.float32, name="lr_g")
    learning_rate_D = tf.placeholder(tf.float32, name="lr_d")
    return inputs_real, inputs_z, learning_rate_G, learning_rate_D

In [7]:

def show_samples(sample_images, name, epoch):
    figure, axes = plt.subplots(1, len(sample_images), figsize = (IMAGE_SIZE, IMAGE_SIZE))
    for index, axis in enumerate(axes):
        axis.axis('off')
        image_array = sample_images[index]
        axis.imshow(image_array)
        image = Image.fromarray(image_array)
        image.save(name+"_"+str(epoch)+"_"+str(index)+".png") 
    plt.savefig(name+"_"+str(epoch)+".png", bbox_inches='tight', pad_inches=0)
    plt.show()
    plt.close()

In [8]:

def test(sess, input_z, out_channel_dim, epoch):
    example_z = np.random.uniform(-1, 1, size=[SAMPLES_TO_SHOW, input_z.get_shape().as_list()[-1]])
    samples = sess.run(generator(input_z, out_channel_dim, False), feed_dict={input_z: example_z})
    sample_images = [((sample + 1.0) * 127.5).astype(np.uint8) for sample in samples]
    show_samples(sample_images, OUTPUT_DIR + "samples", epoch)

In [9]:

def summarize_epoch(epoch, duration, sess, d_losses, g_losses, input_z, data_shape):
    minibatch_size = int(data_shape[0]//BATCH_SIZE)
    print("Epoch {}/{}".format(epoch, EPOCHS),
          "\nDuration: {:.5f}".format(duration),
          "\nD Loss: {:.5f}".format(np.mean(d_losses[-minibatch_size:])),
          "\nG Loss: {:.5f}".format(np.mean(g_losses[-minibatch_size:])))
    fig, ax = plt.subplots()
    plt.plot(d_losses, label='Discriminator', alpha=0.6)
    plt.plot(g_losses, label='Generator', alpha=0.6)
    plt.title("Losses")
    plt.legend()
    plt.savefig(OUTPUT_DIR + "losses_" + str(epoch) + ".png")
    plt.show()
    plt.close()
    test(sess, input_z, data_shape[3], epoch)

In [10]:

def get_batches(data):
    batches = []
    for i in range(int(data.shape[0]//BATCH_SIZE)):
        batch = data[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
        augmented_images = []
        for img in batch:
            image = Image.fromarray(img)
            if random.choice([True, False]):
                image = image.transpose(Image.FLIP_LEFT_RIGHT)
            augmented_images.append(np.asarray(image))
        batch = np.asarray(augmented_images)
        normalized_batch = (batch / 127.5) - 1.0
        batches.append(normalized_batch)
    return batches

In [11]:

def train(get_batches, data_shape, checkpoint_to_load=None):
    input_images, input_z, lr_G, lr_D = model_inputs(data_shape[1:], NOISE_SIZE)
    d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
    d_opt, g_opt = model_optimizers(d_loss, g_loss)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        epoch = 0
        iteration = 0
        d_losses = []
        g_losses = []
        
        for epoch in range(EPOCHS):        
            epoch += 1
            start_time = time.time()
​
            for batch_images in get_batches:
                iteration += 1
                batch_z = np.random.uniform(-1, 1, size=(BATCH_SIZE, NOISE_SIZE))
                _ = sess.run(d_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr_D: LR_D})
                _ = sess.run(g_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr_G: LR_G})
                d_losses.append(d_loss.eval({input_z: batch_z, input_images: batch_images}))
                g_losses.append(g_loss.eval({input_z: batch_z}))
​
            summarize_epoch(epoch, time.time()-start_time, sess, d_losses, g_losses, input_z, data_shape)

In [12]:

# Paths
INPUT_DATA_DIR = "/home/andreas_pappamikail/cloud/images/" # Path to the folder with input images. For more info check simspons_dataset.txt
OUTPUT_DIR = './{date:%Y-%m-%d_%H:%M:%S}/'.format(date=datetime.datetime.now())
if not os.path.exists(OUTPUT_DIR):
    os.makedirs(OUTPUT_DIR)

In [13]:

# Hyperparameters
IMAGE_SIZE = 128
NOISE_SIZE = 100
LR_D = 0.00004
LR_G = 0.0004
BATCH_SIZE = 64
EPOCHS = 300
BETA1 = 0.5
WEIGHT_INIT_STDDEV = 0.02
EPSILON = 0.00005
SAMPLES_TO_SHOW = 5

In [14]:

# Training
input_images = np.asarray([np.asarray(Image.open(file).resize((IMAGE_SIZE, IMAGE_SIZE))) for file in glob(INPUT_DATA_DIR + '*')])
print ("Input: " + str(input_images.shape))

np.random.shuffle(input_images)

sample_images = random.sample(list(input_images), SAMPLES_TO_SHOW)
show_samples(sample_images, OUTPUT_DIR + "inputs", 0)

with tf.Graph().as_default():
    train(get_batches(input_images), input_images.shape)

Input: (3343, 128, 128)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-14-db5a32a0c488> in <module>
      9 
     10 with tf.Graph().as_default():
---> 11     train(get_batches(input_images), input_images.shape)

<ipython-input-11-73fe85d9d0bc> in train(get_batches, data_shape, checkpoint_to_load)
      1 def train(get_batches, data_shape, checkpoint_to_load=None):
      2     input_images, input_z, lr_G, lr_D = model_inputs(data_shape[1:], NOISE_SIZE)
----> 3     d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
      4     d_opt, g_opt = model_optimizers(d_loss, g_loss)
      5 

IndexError: tuple index out of range

Then get the error posted above .. Any help greatly appreciated :)

[Yoriz]

data_shape points to the following tuple (index of each value shown below them)

Output:
(3343, 128, 128)
#  0    1    2

data_shape[3] gives an index error because the index only goes up to 2

[appmkl]

data_shape points to the following tuple (index of each value shown below them)

Output:
 (3343, 128, 128) # 0 1 2 

data_shape[3] gives an index error because the index only goes up to 2

Thank you for the answer ! Yes I figured that was the problem after looking online. So should I just change the data_value to 2 instead of 3 ?

I'm just confused as I followed the. step by step guide on github and only changed the image source, so not sure why the index onnly goes up to 2 ? Could you explain perhaps ?

Thank you for your time !!