<jupyter_start><jupyter_text>First round of experiments  Experiment 1<jupyter_code>model = MLPClassifier(random_state=101, max_iter=500)
model.fit(X_train, Y_train)
sets = ["Training", "Validation", "Testing"]
X_sets = [X_train, X_dev, X_test]
Y_sets = [Y_train, Y_dev, Y_test]

accuracy = {}
for i in range(0,len(X_sets)):  
    pred = model.predict(X_sets[i])
    score = accuracy_score(Y_sets[i], pred)
    accuracy[sets[i]] = score

print(accuracy)<jupyter_output>{'Training': 0.8243943640342458, 'Validation': 0.803193122505373, 'Testing': 0.8222290451335585}<jupyter_text>Experiment 2<jupyter_code>model = MLPClassifier(random_state=101, max_iter=500, hidden_layer_sizes=(100,100))
model.fit(X_train, Y_train)
sets = ["Training", "Validation", "Testing"]
X_sets = [X_train, X_dev, X_test]
Y_sets = [Y_train, Y_dev, Y_test]

accuracy = {}
for i in range(0,len(X_sets)):  
    pred = model.predict(X_sets[i])
    score = accuracy_score(Y_sets[i], pred)
    accuracy[sets[i]] = score

print(accuracy)<jupyter_output>{'Training': 0.8673935577993627, 'Validation': 0.8311329444273872, 'Testing': 0.8520110531163647}<jupyter_text>Experiment 3<jupyter_code>model = MLPClassifier(random_state=101, max_iter=500, hidden_layer_sizes=(100,100,100))
model.fit(X_train, Y_train)
sets = ["Training", "Validation", "Testing"]
X_sets = [X_train, X_dev, X_test]
Y_sets = [Y_train, Y_dev, Y_test]

accuracy = {}
for i in range(0,len(X_sets)):  
    pred = model.predict(X_sets[i])
    score = accuracy_score(Y_sets[i], pred)
    accuracy[sets[i]] = score

print(accuracy)<jupyter_output>{'Training': 0.8494644296848005, 'Validation': 0.8299048203868591, 'Testing': 0.842800122812404}<jupyter_text>Second round of experiments  Experiment 1<jupyter_code>model = MLPClassifier(random_state=101, max_iter=500, hidden_layer_sizes=(50,50))
model.fit(X_train, Y_train)
sets = ["Training", "Validation", "Testing"]
X_sets = [X_train, X_dev, X_test]
Y_sets = [Y_train, Y_dev, Y_test]

accuracy = {}
for i in range(0,len(X_sets)):  
    pred = model.predict(X_sets[i])
    score = accuracy_score(Y_sets[i], pred)
    accuracy[sets[i]] = score

print(accuracy)<jupyter_output>{'Training': 0.8523822321188621, 'Validation': 0.828983727356463, 'Testing': 0.8443352778630642}<jupyter_text>Experiment 2<jupyter_code>model = MLPClassifier(random_state=101, max_iter=500, hidden_layer_sizes=(150,150))
model.fit(X_train, Y_train)
sets = ["Training", "Validation", "Testing"]
X_sets = [X_train, X_dev, X_test]
Y_sets = [Y_train, Y_dev, Y_test]

accuracy = {}
for i in range(0,len(X_sets)):  
    pred = model.predict(X_sets[i])
    score = accuracy_score(Y_sets[i], pred)
    accuracy[sets[i]] = score

print(accuracy)<jupyter_output>{'Training': 0.859024071870081, 'Validation': 0.8219220141234265, 'Testing': 0.8455634019035922}
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