Import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.datasets import imdb (tr, tl), (te, tel) = imdb.load_data(num_words=1000) trm = np.zeros((len(tr), 1000)) fori, x in enumerate(tr): trm[i, x] = 1 tem=np.zeros((len(te),1000)) fori, x in enumerate(te): tem[i, x] = 1 tex = tem[:10000] tey = tel[:10000] tx = trm[10000:] ty = tl[10000:] n = Sequential() n.add(Dense(50, activation = "relu", input_shape=(1000, ))) n.add(Dense(50, activation = "relu")) n.add(Dense(50, activation = "relu")) n.add(Dense(1, activation = "sigmoid")) n.summary() n.compile(optimizer = "adam",loss = "binary_crossentropy",metrics = ["accuracy"]) n.fit(tx,ty,epochs=2) yp=n.predict(tex) print(Actual Label, Predicted Label) print(tey[0],yp[0]) Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz 17464789/17464789 [==============================] - 0s 0us/step Model: "sequential_1" Layer (type) Output Shape Param # ================================================================= dense_3 (Dense) (None, 50) 50050 dense_4 (Dense) (None, 50) 2550 dense_5 (Dense) (None, 50) 2550 dense_6 (Dense) (None, 1) 51 ================================================================= Total params: 55201 (215.63 KB) Trainable params: 55201 (215.63 KB) Non-trainable params: 0 (0.00 Byte) Epoch 1/2 469/469 [==============================] - 4s 5ms/step - loss: 0.4038 - accuracy: 0.8157 Epoch 2/2 469/469 [==============================] - 2s 5ms/step - loss: 0.3081 - accuracy: 0.8669 313/313 [==============================] - 1s 3ms/step