Error when checking input: expected input_61 to have 4 dimensions, but got array with shape (32, 64, 64, 1, 3)

[sandhya9173]

what is utils.py file i Thought its generator discriminator train model but i m getting error in that please can you help me

from future import print_function, division

from keras.datasets import mnist

from keras.layers import Input, Dense, Reshape, Flatten, Dropout from keras.layers import BatchNormalization, Activation, ZeroPadding2D from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam from skimage.io import imread_collection import cv2 import matplotlib.pyplot as plt import random

import sys

import numpy as np

class GAN(): def init(self): self.img_rows = 28 self.img_cols = 28 self.channels = 1 self.img_shape = (self.img_rows, self.img_cols, self.channels) self.latent_dim = 100

    optimizer = Adam(0.0002, 0.5)

    # Build and compile the discriminator
    self.discriminator = self.build_discriminator()
    self.discriminator.compile(loss='binary_crossentropy',
        optimizer=optimizer,
        metrics=['accuracy'])

    # Build the generator
    self.generator = self.build_generator()

    # The generator takes noise as input and generates imgs
    z = Input(shape=(self.latent_dim,))
    img = self.generator(z)

    # For the combined model we will only train the generator
    self.discriminator.trainable = False

    # The discriminator takes generated images as input and determines validity
    validity = self.discriminator(img)

    # The combined model  (stacked generator and discriminator)
    # Trains the generator to fool the discriminator
    self.combined = Model(z, validity)
    self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

def build_generator(self):

    model = Sequential()

    model.add(Dense(256, input_dim=self.latent_dim))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(1024))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(np.prod(self.img_shape), activation='tanh'))
    model.add(Reshape(self.img_shape))

    model.summary()

    noise = Input(shape=(self.latent_dim,))
    img = model(noise)

    return Model(noise, img)

def build_discriminator(self):

    model = Sequential()

    model.add(Flatten(input_shape=self.img_shape))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(256))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(1, activation='sigmoid'))
    model.summary()

    img = Input(shape=self.img_shape)
    validity = model(img)

    return Model(img, validity)

def train(self, epochs, batch_size=128, sample_interval=50):

    # Load the dataset
    #(X_train, _), (_, _) = mnist.load_data()
    col_dir = 'C:/Users/Ajaykumar/Data_set/hack/dataset_2/Images/*.jpg'
    #creating a collection with the available images
    col = imread_collection(col_dir)
    all_images=np.zeros((450,64,64,3))#64*64 Number of pixels
    X=np.zeros((450,64,64,3))
    for i in range(450):
        #if i%10==0:
            #print(i)
        var=cv2.resize(col[i],(64,64))
        #print(var)
        all_images[i,:,:]=var
        all_images[i,:,:]=all_images[i,:,:]
        #X_train[i,:,:] = all_images[i,:,:]
    #print(np.shape(X_train))

    # Rescale -1 to 1
    all_images = all_images / 127.5 - 1.
    all_images = np.expand_dims(all_images, axis=3)

    # Adversarial ground truths
    valid = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):

        # ---------------------
        #  Train Discriminator
        # ---------------------

        # Select a random batch of images
        #random.sample(range(0, dataset.shape[0]), n_samples)
        idx = random.sample(range(0, all_images.shape[0]), batch_size)
        imgs = all_images[idx]

        noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

        # Generate a batch of new images
        gen_imgs = self.generator.predict(noise)

        # Train the discriminator
        d_loss_real = self.discriminator.train_on_batch(imgs, valid)
        d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        # ---------------------
        #  Train Generator
        # ---------------------

        noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

        # Train the generator (to have the discriminator label samples as valid)
        g_loss = self.combined.train_on_batch(noise, valid)

        # Plot the progress
        print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

        # If at save interval => save generated image samples
        if epoch % sample_interval == 0:
            self.sample_images(epoch)

def sample_images(self, epoch):
    r, c = 5, 5
    noise = np.random.normal(0, 1, (r * c, self.latent_dim))
    gen_imgs = self.generator.predict(noise)

    # Rescale images 0 - 1
    gen_imgs = 0.5 * gen_imgs + 0.5

    fig, axs = plt.subplots(r, c)
    cnt = 0
    for i in range(r):
        for j in range(c):
            axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
            axs[i,j].axis('off')
            cnt += 1
    fig.savefig("C:/Users/Ajaykumar/Data_set/hack/images/aa/%d.png" % epoch)
    plt.close()

if name == 'main': gan = GAN() gan.train(epochs=30000, batch_size=32, sample_interval=200)

[sandhya9173]

hi I m added my data set can you please this code is low to high-resolution images code is their are not

[diegoalejogm]

Hey, I'll take a look this weekend. Hope it is not too long of a wait 👍

[Player0109]

I think this problem is arrising due to this line: all_images = np.expand_dims(all_images, axis=3)

This Line is not needed.