Upload imdb_rnn.py

imdb_rnn.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+
+
+from numpy import argmax
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import RMSprop, Adam
+from tensorflow.keras.datasets import imdb
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from sklearn.metrics import accuracy_score
+import pickle
+
+top_words = 5000
+(X_train, y_train), (X_test,y_test) = imdb.load_data(num_words=top_words)
+
+max_review_length = 500
+X_train = pad_sequences(X_train, maxlen=max_review_length)
+X_test = pad_sequences(X_test, maxlen=max_review_length)
+
+model=tf.keras.models.Sequential([
+   tf.keras.layers.Embedding(input_dim=top_words,output_dim= 24, input_length=max_review_length),
+   tf.keras.layers.SimpleRNN(24, return_sequences=False),
+   tf.keras.layers.Dense(64, activation='relu'),
+   tf.keras.layers.Dense(32, activation='relu'),
+   tf.keras.layers.Dense(1, activation='sigmoid')
+])
+
+# compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+print("----------------------  -------------------------\n")
+
+# summarize the model
+print(model.summary())
+
+print("----------------------  -------------------------\n")
+
+early_stop = tf.keras.callbacks.EarlyStopping(monitor='accuracy', mode='min', patience=10)
+
+print("----------------------  Training -------------------------\n")
+
+# fit the model
+model.fit(x=X_train,
+         y=y_train,
+         epochs=100,
+         validation_data=(X_test, y_test),
+         callbacks=[early_stop]
+         )
+print("----------------------  -------------------------\n")
+
+
+def acc_report(y_true, y_pred):
+   acc_sc = accuracy_score(y_true, y_pred)
+   print(f"Accuracy : {str(round(acc_sc,2)*100)}")
+   return acc_sc
+
+
+preds = (model.predict(X_test) > 0.5).astype("int32")
+print(acc_report(y_test, preds))
+
+model.save(r'C:\Users\shahi\Desktop\My Projects\DeepPredictorHub\RN.keras')