ValueError: Input 0 of layer "Discriminator" is incompatible with the layer: expected shape=(None, 3), found shape=(100, 2)

ss_pheonix: <pre><code class="lang-auto">import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import precision_score, recall_score, f1_score,\
                            accuracy_score, balanced_accuracy_score,classification_report,\
                            plot_confusion_matrix, confusion_matrix
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.model_selection import train_test_split

import lightgbm as lgb
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply, Concatenate
from tensorflow.keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D, LeakyReLU
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initializers import RandomNormal
import tensorflow.keras.backend as K
from sklearn.utils import shuffle

import pickle
from tqdm import tqdm

import numpy as np

from scipy import stats
import pandas as pd


np.random.seed(1635848)

def get_data_XYZ_one_dimensional(n, a=-2, c=1/2, random_state=None, verbose=True):
    """
    Generates pseudo-random data distributed according to the distribution defined in section 2.1 of the document
    "Math/Confounders and data generation.pdf".
    :param n: Number of data points to generate.
    :param a: Mean of X.
    :param c: Shape parameter for Weibull distribution.
    :param random_state: Used to set the seed of numpy.random before generation of random numbers.
    :param verbose: If True will display a progress bar. If False it will not display a progress bar.
    :return: Pandas DataFrame with three columns (corresponding to X, Y and Z) and n rows (corresponding to the n
    generated pseudo-random samples).
    """
    np.random.seed(random_state)

    output = []
    iterator = tqdm(range(n)) if verbose else range(n)
    for _ in iterator:
        X = stats.norm.rvs(loc=-2, scale=1)
        Y = stats.bernoulli.rvs(p=1/(1+np.exp(-X)))
        if Y == 0:
            Z = stats.expon.rvs(scale=np.exp(-X))  # note: np.exp(-X) could be cached for more computational efficiency but would render the code less useful
        elif Y == 1:
            Z = stats.weibull_min.rvs(c=c, scale=np.exp(-X))
        else:
            assert False
        output.append((X, Y, Z))

    return pd.DataFrame(output, columns=["Personal information", "Treatment", "Time to event"])

data = get_data_XYZ_one_dimensional(n=100, random_state=0)
print(data)


# The Architecture of CGAN

class cGAN():
    
    """
    Class containing 3 methods (and __init__): generator, discriminator and train.
    Generator is trained using random noise and label as inputs. Discriminator is trained
    using real/fake samples and labels as inputs.
    """
    
    def __init__(self,latent_dim=100, out_shape=3):
        
        self.latent_dim = latent_dim
        self.out_shape = out_shape 
        self.num_classes = 2
        # using Adam as our optimizer
        optimizer = Adam(0.0002, 0.5)
        
        # building the discriminator
        self.discriminator = self.discriminator()
        self.discriminator.compile(loss=['binary_crossentropy'],
                                   optimizer=optimizer,
                                   metrics=['accuracy'])

        # building the generator
        self.generator = self.generator()

        noise = Input(shape=(self.latent_dim,))
        label = Input(shape=(1,))
        gen_samples = self.generator([noise, label])
        
        # we don't train discriminator when training generator
        self.discriminator.trainable = False
        valid = self.discriminator([gen_samples, label])

        # combining both models
        self.combined = Model([noise, label], valid)
        self.combined.compile(loss=['binary_crossentropy'],
                              optimizer=optimizer,
                             metrics=['accuracy'])


    def generator(self):
        init = RandomNormal(mean=0.0, stddev=0.02)
        model = Sequential()

        model.add(Dense(128, input_dim=self.latent_dim))
        model.add(Dropout(0.2))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))

        model.add(Dense(256))
        model.add(Dropout(0.2))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))

        model.add(Dense(512))
        model.add(Dropout(0.2))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))

        model.add(Dense(self.out_shape, activation='tanh'))

        noise = Input(shape=(self.latent_dim,))
        label = Input(shape=(1,), dtype='int32')
        label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
        
        model_input = multiply([noise, label_embedding])
        gen_sample = model(model_input)

        model.summary()


        return Model([noise, label], gen_sample, name="Generator")

    
    def discriminator(self):
        init = RandomNormal(mean=0.0, stddev=0.02)
        model = Sequential()

        model.add(Dense(512, input_dim=self.out_shape, kernel_initializer=init))
        model.add(LeakyReLU(alpha=0.2))
        
        model.add(Dense(256, kernel_initializer=init))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.4))
        
        model.add(Dense(128, kernel_initializer=init))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.4))
        
        model.add(Dense(1, activation='sigmoid'))
        
        gen_sample = Input(shape=(self.out_shape,))
        label = Input(shape=(1,), dtype='int32')
        label_embedding = Flatten()(Embedding(self.num_classes, self.out_shape)(label))

        model_input = multiply([gen_sample, label_embedding])
        validity = model(model_input)

        model.summary()


        return Model(inputs=[gen_sample, label], outputs=validity, name="Discriminator")




    def train(self, X_train, y_train, pos_index, neg_index, epochs, sampling=False, batch_size=32, sample_interval=100, plot=True): 
        
        # though not recommended, defining losses as global helps as in analysing our cgan out of the class
        global G_losses
        global D_losses
        
        G_losses = []
        D_losses = []
        # Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))

        for epoch in range(epochs):
            
            # if sampling==True --&gt; train discriminator with 8 sample from positive class and rest with negative class
            if sampling:
                idx1 = np.random.choice(pos_index, 3)
                idx0 = np.random.choice(neg_index, batch_size-3)
                idx = np.concatenate((idx1, idx0))
            # if sampling!=True --&gt; train discriminator using random instances in batches of 32
            else:
                idx = np.random.choice(len(y_train), batch_size)
            samples, labels = X_train[idx], y_train[idx]
            samples, labels = shuffle(samples, labels)
            
            # Sample noise as generator input
            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
            gen_samples = self.generator.predict([noise, labels])

            # label smoothing
            if epoch &lt; epochs//1.5:
                valid_smooth = (valid+0.1)-(np.random.random(valid.shape)*0.1)
                fake_smooth = (fake-0.1)+(np.random.random(fake.shape)*0.1)
            else:
                valid_smooth = valid 
                fake_smooth = fake
                
            # Train the discriminator
            self.discriminator.trainable = True
            d_loss_real = self.discriminator.train_on_batch([samples, labels], valid_smooth)
            d_loss_fake = self.discriminator.train_on_batch([gen_samples, labels], fake_smooth)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

            # Train Generator
            self.discriminator.trainable = False
            sampled_labels = np.random.randint(0, 2, batch_size).reshape(-1, 1)
            # Train the generator
            g_loss = self.combined.train_on_batch([noise, sampled_labels], valid)

            if (epoch+1)%sample_interval==0:
                print('[%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f'
                  % (epoch, epochs, d_loss[0], g_loss[0]))
            G_losses.append(g_loss[0])
            D_losses.append(d_loss[0])
            if plot:
                if epoch+1==epochs:
                    plt.figure(figsize=(10,5))
                    plt.title("Generator and Discriminator Loss")
                    plt.plot(G_losses,label="G")
                    plt.plot(D_losses,label="D")
                    plt.xlabel("iterations")
                    plt.ylabel("Loss")
                    plt.legend()
                    plt.show()


data.Treatment.value_counts()

scaler = StandardScaler()

X = scaler.fit_transform(data.drop('Treatment', 1))
y = data['Treatment'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

lgb_1 = lgb.LGBMClassifier()
lgb_1.fit(X_train, y_train)

y_pred = lgb_1.predict(X_test)

# evaluation
print(classification_report(y_test, y_pred))
plot_confusion_matrix(lgb_1, X_test, y_test)
plt.show()


le = preprocessing.LabelEncoder()
for i in ['Personal information',	'Treatment',	'Time to event']:
    data[i] = le.fit_transform(data[i].astype(str))

y_train = y_train.reshape(-1,1)
pos_index = np.where(y_train==1)[0]
neg_index = np.where(y_train==0)[0]
cgan.train(X_train, y_train, pos_index, neg_index, epochs=500)

</code></pre>
<p>Here, the training gives an error ValueError: Input 0 of layer “Discriminator” is incompatible with the layer: expected shape=(None, 3), found shape=(100, 2). Well I understand I have to fix the shape by changing the input but where and how to do it. Also there are 3 columns in data so how to go about making this work?</p>

ptrblck: <p>Based on your code it seems you are using Tensorflow with Keras, so I would recommend to post this question in their discussion board as you would find the experts there. <img src="https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=12" title=":slight_smile:" class="emoji" alt=":slight_smile:" loading="lazy" width="20" height="20"></p>