tensorflow 2.x.x version from TexCNN

archive/tensorflow/v2/2-1.TextCNN/TextCNN.py

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+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import one_hot
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Embedding, Conv1D, MaxPooling1D, Flatten, Dense
+import numpy as np
+'''
+tensorflow version 2.14.0
+'''
+# Data
+sentences = ["i love you", "he loves me", "she likes baseball",
+             "i hate you", "sorry for that", "this is awful"]
+labels = np.array([1., 1., 1., 0., 0., 0.])
+
+# Data preprocessing
+word_list = []
+for i in sentences:
+    word_list.append(i.split(" "))
+
+embedding_size = len(sentences)
+sentences_encode = [one_hot(d, embedding_size) for d in sentences]
+
+x_train = pad_sequences(sentences_encode, maxlen=6)
+
+# Model
+model = Sequential([
+    Embedding(input_dim=embedding_size, output_dim=32, input_length=6),
+    Conv1D(8, (3), activation='relu'),
+    MaxPooling1D(),
+    Flatten(),
+    Dense(1, activation='softmax')
+])
+
+model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['acc'])
+
+model.fit(x_train, labels, epochs=100)