Create ser.py

ser.py

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+from sklearn.neural_network import MLPClassifier
+
+from sklearn.metrics import accuracy_score
+from utils import load_data
+
+import os
+import pickle
+
+# load RAVDESS dataset
+X_train, X_test, y_train, y_test = load_data(test_size=0.25)
+# print some details
+# number of samples in training data
+print("[+] Number of training samples:", X_train.shape[0])
+# number of samples in testing data
+print("[+] Number of testing samples:", X_test.shape[0])
+# number of features used
+# this is a vector of features extracted 
+# using utils.extract_features() method
+print("[+] Number of features:", X_train.shape[1])
+# best model, determined by a grid search
+model_params = {
+    'alpha': 0.01,
+    'batch_size': 256,
+    'epsilon': 1e-08, 
+    'hidden_layer_sizes': (300,), 
+    'learning_rate': 'adaptive', 
+    'max_iter': 500, 
+}
+# initialize Multi Layer Perceptron classifier
+# with best parameters ( so far )
+model = MLPClassifier(**model_params)
+
+# train the model
+print("[*] Training the model...")
+model.fit(X_train, y_train)
+
+# predict 25% of data to measure how good we are
+y_pred = model.predict(X_test)
+
+# calculate the accuracy
+accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)
+
+print("Accuracy: {:.2f}%".format(accuracy*100))
+
+# now we save the model
+# make result directory if doesn't exist yet
+if not os.path.isdir("result"):
+    os.mkdir("result")
+
+pickle.dump(model, open("result/mlp_classifier.model", "wb"))